National Criminal Justice Reference Service nCJrs

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I. MICROCOPY RESOLUTION TEST CHART NATIONAL BUREAU Of STANOARDS-1963-A

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Points of view or opinions stated in this document are those of the author(s) and do not represent the official position or policies of the U. S. Depal1m,~nt of Justice. . 4-23-82 i . t National Institute of Justice United States Department of Justicle Washington, D. C. 20531 '. , o

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~ II State of New York's ACKNOWLEDGEMENTS LATENT FINGERPRINT I IDENTIFICATION SYSTEM REPORT I The search algori'thm described in this report was developed by Mr. Stephen CRIMINAL JUSTICE RESEARCH AND DEVELOPMENT Ferris of DCJS with assistance fro~ Mr. Marvin Israel of Arthur Young & Company. BUREAU I Mr. Israel.'s sugge~tions and assistance were invaluable in the formulation and I development of the algorithm.

System programming was done by Mr. Michael Wierzbowski of DCJS. Throughout NEW YOltK STATE DIVISION OF IIe' CRHfINAL JUSTICE SERVICES the project, he simplified and corrected the author's logical structures and assisted with the development of both the test programs and production system. u.s. Department of JUlltice ~ National Institute of Justice I This document has been reproduced exactly as received from the Lastly, many thanks to Miss Patricia Smrtic who so diligently typed the person or organization originating it. Points of view or opinions stated in this document are those of the authors and do not necessarily HUGH L. CAREY represent the official position or pOlicies of the National Institute of I many revisions of this document. Justice. Governor

Permission to reproduce this ~,tglited material has been granted by Public DOIDajn I FRANK J. RCX;ERS Bureall of ,Justice Statistics Commissioner to the National Criminal Justice Reference Service (NCJRS). I Further reproduction outside of the NCJRS system requires permis­ sion of the ~ owner. ADAM F. D' ALESSANDRO Deputy Commissioner I

TERRY K. LINDH I Director, Research & Development I WILLIAM J. FITZGERALD Project Director I

" ,I STEPHEN G. FERRIS I Author NCJRS I DEC 16 1981 I i " ACQ 1J 13!T: nJ~s 1981 I / - o I' I , i . I I ; I ' PART I I ABSTRACT II 1.0 Executive Summary Part 1 of this document describes the candidate ranking algorithm developed I .'II The following conclusior.s have been inferred on the basis of the information for use in the DCJS Latent Fingerprint Identification System and evaluates its presented in this document: ' I I use within the agency's operating envi l~onment. 'I 1. For all penal law charge categories, the candidate ranking algorithm The algorithm ranks candidates on the basis of fingerprint descriptors and improvement over methods currently employed at I three probability factors mentioned below. Computer disk files were created to .~ DCJS. When pro/Jerly incorporated into a production system, a long­ capture data n~l ating to: ~' I· I r,~'1 ter~ increase In the absolute number of identifications will occur. 1. The alge frequency distribution of persons arrested within New York ! ,I '~ I 2. An lmprovemenf of approximately 52% over random expected value (list Static foY' each of 12 penal law charge categories by sex and subdivision I ",ri""'1 I position) in the sample was noted. For the charge categories of murder of the state (upstate or New York City). I , I I and arson, the improvement in list position was approximately 35%. I 2. The probabi1ity of rearrest on any of the 12 charges after an arrest "1 3. Of 37 one-finger searches with accurate ridge counts and known finger on a first charge over a ten-year per'iod from the date of first arrest. I J I 14 suspect lists were less than 100 candidates; 6 additional I 3. Th€! probability of a male or female at any age between 16 and 65 being than 200 and 3 less than 300. For two-finger searches, as arrested on any of the 12 charges. I I above, less than 100; 3 "/ere less than 200. For three- ThE: algorithm is 0. linear combination of elements of these files and a finger searches, all lists were less than 100 candidates to the target [ factor derived from sums of differencesof latent and file print ridge cour.ts. I cand~date. This data is presented graphically in FIGURE 1. A sample was selected and programs written to test the ranking procedure. The

" , 4. At CJrrent staff levels, DCJS can increase the probability of an identifi­ I test results are presented and incorporated into a cost model for the DCJS , 'II;;, cation by a factor of three and at the same time the number of cases Special Services' Unit. I ; pr)cessed per year to approximately 1000. Of the one-thousand cases, Ir I Part 2 analyzes the development of similar systems by other criminal I ) i i " , '\ approximate 1y 250 would be computer searchable. If a lower probabil ity ,I ,' justice age~cies. 'j I I of identification is chosen, case volume can be increased accordingly. [ 5. A search on social descriptors alone will be possible under restricted I conditions. With crime type, location and suspect identifying data, a I,

vi candidate list can be produced that selects individuals on the basis of ",,; ~ II ) I \ rn conformity to a model offender's arrest history, age and sex. ~ i,i

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1.1 Recommendations

1. The system developed by Rand 0 should be implemented. When testing ,../'. in the prpduction environment "is complete, remove the pilot system. 2. In-;depth explol"ation of modifications to the eXisting arrest fingerprint ~ ',- system and/or CCH data base to enhance throughput time. 3. Agency support for "front-end" system enhancement. This support takes the specific form of development of an image processing algorithm which will enable more acc~rate estimation of latent impression pattern types and ridge counts.

4. When the system is in place, solicitation of submission'of latent impressions from police agencies within New York State.

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I r I 2.0 Overview of age and pattern of arrest. DCJS maintains an operational unit dedicated to the identification of I !1 The Rand D System, in effect, provides data about offenders for twe1ve­ latent fingerprint images. Evidence is presented to the examiner and data 1] charge categories. I necessary for the search is extracted. At DCJS, this takes the form of a L: Secondly, the system ranks candidates on fingerprint data. The pilot system pattern type classification, ridge count and estimation of likely finger number. I /] employed a ridge count tolerance and pattern type elimination independent of the (See APPENDIX 1). number of 1atents input. Consequently, in multiple finger searches, much available Prior to the 1970's, a completely manual latent file system was maintained. I I~ information was ignored. By application of algorithm RH, described in APPENDIX 2, In 1974 a pilot (3-county) computer assisted retrieval system was developed and all available fingerprint data ;s utilized. I implemented. Although its logic was simplistic, it provided enhanced performance I~ , and throughput over manual "single finger" files. On the basis of this performance I the agency elected to develop a more powerful statewide operating system. ~ Latent fingerprint identification at DCJS is an instance of selecting a IIIii: I L set of very similar records fran a 1arge computer file, and thereafter, manua lly I. . I verifying or rejecting each record so selected against photographs of 'latent II impressions. Typically, input to the examiner consists of one or more finger­ I print impressions and a brief narrative description of the crime. The impression I may not be of sufficiently high quality to allow an accurate determination of I pattern type, ridge count and a number of finger positions may appear equiprobable. I

If a ridge count tolerance of ~ 4 is chosen, a single finger search of a high

\ ~ : I I population density county can yield in excess of 40,000 candidates with previous , i I arrests on ~he crime-search charge. Clearly, a more sophisticated method of I

"\. candidate selection is required if the latent search is to be practical. ,~' I The Rand D System differs conceptually from the pilot system in two rn

-. "i important respects. Firstly, the system employs statistical data, described I in SECTION 3 of this document, to rank candidates by their similarity to a ~ .~ model offender, Analysis indicates that persons first arrested on anyone Cl I D of the twelve-charge categories are, in general, similar to each other in terms ,"" i

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I Factors for inclusion in the algorithm were the offender's: 3.0 Search Algorithm I The search algorithm described herein is a linear combination of probabilities, • age • sex discussed below, and a factor de'r'ived fran fingerprint data. • arrest history I 3.1 Fingerprint Factor The mechanism for ranking fingerprints on the basis of ridge count differences • race For technical reasons, race could not be addressed and was re1e gat ed t 0 an I was developed and tested by Mr. Robert Hall, Rand D Consulting Statistician, at elimination factor in the production system. I DCJS. 3.2.1 Data Extraction and Analysis Let ril and r denote ridge counts for fingers ie: {l , .•• , 10 } and file (f) if Computer programs were written to extract data from the agency's criminal I or latent (1) impressions for IS{ 1, ... , 10 }. Thent \ history data base. Every third record of the CCH files was examined. These files S,= . L Irif-rill (1) ,e:I contain extensive information about persons arrested within New York State. For I In practice at DCJS: r is an integer such that 0< r~26 and a t·olerance, n, f obtaining arrest history data including arrest charge, date and place, inquiries is imposed on the difference of ridge counts on each finger. Specifically, if I are made to a data base structure called the NYSEVENTSET which contains the data I rif-rnl >n, the record is rejected. For test purposes, n was set equal to 1; indicated above. I in producti on n ~ 4. Information for age frequency distributions, as described below, was obtained If the latent being searched is an arch or tented arch, sl=lx(number of arches) from Unified Crime Reporting Files (UCR) for calendar years 1977, 1978 and 1979.

\ I + score for non-arches. 3.2.1.1 Age Frequency Distribution This ranking procedure is highly effective on ten-finger (arrest) searches New York State has traditionally been divided into ten geographic regions, I and useful discrimination in latent searches. A performance summary is prov~des consisting of groups of counties. TABLES 1 and 2 contain the correspondence between given in APPENDIX 2 of this document. county and county code and region and county codes, respectively. I ! , I' 3.2 Social Factors Within the UCR files, data is stored which indicates the age, sex and

The following general assumptions were made: i' I charge for arl~estees within New York State by calendar year. The set of UCR charges 1. Statistically, the behavior of repeat offenders within New York State, ; ( "'\. I was divided into twelve latent identification system charge categories. The .~ as manifested by arrest data, was quantifiable as described below. I correspondence between Penal Law Articles and Latent Charge Codes is given in 2. Arrest history is strongly correlated with overall criminal behavior. I TABLE 3. Raw data to produce age frequency distribution for each region, both I Thi s assumpti on attempted to make the i ntuiti on of the experi enced ' I sexes and the 12 charge c1asse$ was extracted. Graphs of selected age frequency I latent fingerpdnt examiner more exact: I 6 , 5 .. ~ I I I ! r-I 1 ,! I ~ j I II ~ I I , NE~v YORK STATE AREA CODES I NEH YORK STATE COWITY CODES @ I f!, 1 I n II County Code County Code t'/ tI I AREA/CODE COur·ITY CODES Albany 01 Oneida 32 il Allegany 02 Onond3.ga 33 Capital District/Ol 01 I Broome 03 Ontario 34 J 41 Cattaraugus 04 Orange . 35 t. I I 46 Cayuga 05 Orleans 36 ·'1 ~ Chautaugua 06 OSlo/ego 37 ~~I ' I Chemung 07 Otsego .38 Hudson Val1ey/02 10 35 Putnam 39 l j I ~henango 08 ,",',: 13 39 Clinton 09 Queens 40 ~ 19 52 I Rensselaer 41 Columbia 10 II ! Cortland 11 Richmond 42 Delaware 12 Rockland 43 . j ~ Hestchester/Rockiand/03 43 " I Dutchess 13 St. lawrence 44 rl r 59 Erie 14 Saratoga 45 1 Essex 15 Schenectady 46 Franklin 16 Schoharie 47 Eri e/Ni agar-a/04 14 . I Fulton 17 Schuyler 48 1 I r" ~ 31 Genesee 18 Seneca 49 Greene 19 Steuben 50 l' . I < Hamilton 20 Suffolk 51 Southern Tier/OS 02 05 50 . e Herkimer 21 Sull ivan 52 OJ rn .. " I f 00 • 03 07 53 , Jefferson 22 Tioga 53 04 . 48 Kj,ngs 23 Tompkins 54 j rn 54 I lewi s 24 Ulster 55 ~larren 56 livingston 25 ~:es tern/Do 0'-c 27 49 . 61 t~ashington 57 Nadison 26 14~ 18 3'1 58 I Nonroe 27 \~ayne 58 25 36 . 60 Hontgomery 28 ~lestchester 59 3 Nassau 29 ~Iyoming 60 Ne\'I· York 30 Yates 61 ~ I Centra1/Hohawk Valley/07 08 21 33 . i' ~ Niagara 31 Bronx 62 11 26 37 .12 28 38 ( U 17 32 47 .. - I 0 Northern/DB 09 22 55 E 57 0 " , '" 15 2 J ,,, 16 44 5G [ TABLE 1 U 20 45

" [ U long Island/Og 29 -,"'\-"/ 51

[~ . fie.", York City/10 ." 0 23 .t:2 30 62 40 [ n TABLE 2

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[ I) I I I SYSTH1 CODE PENAL LAW ARTICLE DESCRIPTION [ distributions are presented in APPENDIX 3. 01 120 Murder/Manslaughter I 3.2.1.2 Sex Code Mix [ 02 125 Assault I Data was extracted to provide the probability of a male or female at [ 03 130 Sex Offenses each age from 16 through 65 and within each region being arrested on any of the 04 140. Burglary/Offense against latent system charge codes. Thus, if Pm and Pf are the probabilities of ' arrest property I I of a male or female on any specific charge at age k within any specific region, [ 05 145. Criminal Mischief i I Pf+Pm=l. Graphs of selected data elements are presented in APPENDIX 4. 06 150. Arson [ 3.2.1.3 Arrest History Data 07 155. Larceny I I The arrest history data was extracted to yield the probability of [ 08 160. Robbery ., ,I r i an individual arrested for the first time on charge C in year Y, being rearrested 09 165 Theft ~ during any bi-annual interval on charge C, or C or ... C . Files were created [ 10 220 Controlled substance ,I 2 12 '! I for year of first arrest 1969 through 1976, ten regions, 12 first arrest charge 11 265 Weapon~ categories and both sexes. [ 12' All Other ------~, , Graphs of the resulting probabilities versus bi-annua1 intervals to ten [ years of first arrest date, are presented in APPENDIX 5. rn 3.2.2 Simplification of Data Files

~ .j: It was not possible to make simplifying distributional assumptions about /" ill the extracted data. Probabilities were computed from the data and plots were I ~ made of probability versus time unit. The usual non parametric statistical test, i' 2 such as the chi , could not be used effectively. An empirical examination of the , \ ,,' ~ TABLE 3 ~ data was made with hope of simplification. I 'I On the basis of this examination, the follONing simplifications were made: , , n 1. Adaptation of a unified lO-year arrest history. The arrest history l";t: ,\ : i I function appeared to be time invarient in the sense that it was I' .~ u I independent of the date of first arrest. Specifically: for application u purpose, the time versus rearrest probability function is invariant -; ,.' under a time axis translation. i I t, 0 , ) 7 it: I 0 i ' , I ' ------~-- ---~ I

I If 2. Consolidations of the ten regions into "upstate" and "downstate" Then S= r . S = L A- S,_ (5) ,=- l' I where "downstate" consists of the five boroughs of New York City. is the aggregate.score. I 3. Consolidations of sex code mix into one aggregate for the time As incorporated into the test search programs, described below, or the period examined. production system main search, the search algorithm has the following form: I 4. Consolidation of the age frequency distribution into an aggregate 1. Read an arrest fingerprint file record. If the pattern type of the derived from the data over the years used in the sample. selected input finger position I:!quals the file pattern type, continue I Format and content of the computer disk files containing the consolidated else read the next record. data is given in APPENDIX 6. 2. Check the reported age. If within established tolerances, continue - I 3.3.0 Age Factor else read the next record. Let Pag be the probability that an individual on file with reported 3. For file loop or whorl pattern types, check the ridge counts. If for I age k and known sex such that 16'::'k '::'65, is arrested on the search charge. any finger, 1r i -rif I> n (n, the established tolerance), r-ead the next I Then: S2=(1-Pag ) (2) record - else compute equation (1). 3.3.1 Sex Code Factor 4. Store ID number, reported age, sex code and Sl' Let P be the probability that an inoividual with an arrest history and I s 5. Do 1 through 4 through detail fingerprint record exhaustion. of age k, such that 16'::'k'::'65, is male or is female is arrested on the search charge. ( 6. Sort the storage file by 10 number eliminating the duplicates created Then: S3 =(l-P ' s ) (3) by referencing fingerprint classification in the master file. 3.3.2 Arrest History Factor 7. Read criminal history records by io number from the file of 4, above. iJj, I Let P be the probability that an individual with an arrest history is ~ ~', ' ar 8. Eliminate records on county code. I rearrested on the search charge - some integer number between 1 and 20 bi- ~ 9. Construct an arrest history (SECTION 4.2.2.2) for the record.

,'i annual intervals from his date of first arrest on anyone of the twe1ve system J, 10. Eliminate on charge code. f- " m charge categories. U 11. If race code was entered, eliminate on race code. Then: SIf=(l-P ) (4) 12. Compute S2 from equation (2). -~ ar 3.4 Let r = (71. ' A 2' A 3' A 4) be a four dimensional vector. The elements of this U r' 1 13. Compute S3 from equation (3). vector are real numbers. They are empirically determined weights used in the 14. Compute S4 from equation (4). '. , ~ ~ j ~ 0 F scoring procedure, as described below. .' 15. Compute S from equation (5). . 1 ! Let S = (Sl' S2' S3' S4) be a 4-vector containing the score factors of 16. Store S and the candidate's sex code, YOB and 10 number. t U 0 1 ), equations (1) through (4). t-S, 17. Continue to exhaustion of the storage file. • 1 ,, n { ~ 8, 0 t , ' 9 ~., 1 I { '1 .; ";1 , ~-\ [] l·_l tI ' .. " / I I 4.0 Statement of the Problem 4.1 The Sample [ Let { S} be a set ·of functions of r such that Domain (S)= {X I X is a probability file element or X is an integer such that O

Forma ·····\11 .... , y, the problem was to determine minT( An)' i.e., to find a An candidate's fingerprint classification is presented in APPENDIX 8. (7) u The sample chosen is clearly not random. 10 u n

,/ - I I FP MASTER DETAIL RECORD It cannot be considered representative of the input to any DCJS identification The master arrest fingerprint file contains approximately 4.5 million records. I system in terms of distribution by'county, charge code or personal characteristics Bit format is presented below. The file can be accessed in three ways: of the arrestees. [ • sequential READ of the entire file 4.2 Test Programs A • by 10 number [ For simplicity, the programs to determine . 11 were accomplished in four • by so-called sex pattern type (the record key) stages: The program's functional characteristics are given below. Detail fingerprint records have the following format: [ 4.2.1 TESTSRCH 1 ABBREVIATION POSITION (WORD: START BIT:LENTGH) COMMENT TESTSRCH 1 accomplished a sequential search of FPf·1ASTER, the master [ FPSEX 0: 47:01 M=O, F=l fingerprint file. FPMASTER is a hashed access file with detail records formatted FPPAT01 0: 46:03 Pattern type FPPATn 0: 46-3{n-l):03 n= 1 , ... ,10 as in TABLE 4. FPYOB 0: 16:07 Year of birth [ FPRCTOl 0: 09:05 Ridge count 01 4.2.1.1 Input FPRCT02 0: 04:05 Ridge count 02 FPRCT03 1: 47:05 Ridge count 03 [ Input to the program was by punched card as follows: . 1. F/P descriptors (pattern type/ridge count tolerance and finger FPRCT10 1: 12: 05 Ridge count 10 FPFLAG1 1: 07: 01 Value 0 [ position). FPFLAG2 1: 06:01 Value 0 FPFLAG3 1: 05:01 Value 0 2. Associated search number. Search numbers range from 01 to 50. FPMOB 1: 04:04 Month of birth [ FPFLAG4 1: 00:01 Value 1 \ 4.2.1.2 Functional Description FPFLAG5 2: 47:01 Value 1 FPDOB 2: 46:05 Day of birth TESTSRCH 1 performed the following functions: FPFINIT 2: 41 :05 First initial .... '. ;i FPMINIT 2: 36:05 ~1idd1e Initial U 2: Last initial , 1, Read every detai 1 record of FP~lASTER. , , FPLINIT 31 :05 FPCODE 2: 26:02 Record type , n FPIDTYP 2: 24:01 10 number type 11 L.! 2. For searches 1 through 50, eliminated records on the basis of \ ; ~ FPID 2: 23:23 10 number . (ot fingerprint pattern type. I j .: i iI I ~ 3. Eliminated on year of birth tolerance. ~ ( f ~ \ 4. Eliminated non-criminal prints. I D 5. Eliminated records on the basis of ridge count tolerance taken I

.• II at n=+l. .:. i TABLE 4 0il ~J 6. Computed selected records score using equation (1). 0:j 7. Wrote the data elements of 4.2.1.3, below, to a disk file.

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I I 4.2.1.3 Output 4.2.2.1 Input I TESTSRCH output was a disk file containing one-word records formatted 1. Candidate list stored in TEST 1 files as follows: 2. Data from a disk file, TESTINPUT, consisting of data elements from I I APPENDIX 7. Records of this file contained: FIELD START BIT: LE~GTH Field Word: Start bit: length

I Search number (1-50) 47:06 I Search Number 0: 47:06 ( Match Indicator 41:02 O=finger 2 I ID#/Type 0: 41:24 l=finger 2 and 3 County Code 0: 17:06 [ 2=fingers 2, 3 and 4 Crime Code 0: 11 :04 17:08 10 Type indicator 39:01 O=NYSID, l=Green Other Search Charges 0: 47:08 [ 10 Number (NYSID or Green) 38:23 Crime Year 1: 1 : 39:04 [ Year of Birth 15:07 Crime Month Sex Code 08:01 O=male I 4.2.2.2 Functional Description ,1 TESTSRCH 2 performed the following functions: [ l=female -I ~ FP Score fingers 1, 5-10 07:05 J 1. Accessed criminal history files by 10 number.

I, -1 2. Checked the county code for an event at a NYSID number. If the U,1 FP Score finger 4 02:01 ~ FP Score finger 3 01 :01 I county code equaled the event county code, examined the record; if 0n FP Score finger 2 00:01 rn not, checked the next record. being 3. Eliminated events not indicating a felony or misdemeanor arrest. U Since multiple searches employing different finget' combinati ons were ~ executed it was space efficient to produce fingerprint factors at a later stage 1 4. Eliminated events on the basis of selected charge codes. 5. Constructed so-called arrest hi~tories for chosen events. These were ~'1 of processing, i.e., to produce separate records based on fingers being searched. ~ 4.2.2 TESTSRCH 2 twelve-words in length a~d contained 12-bit mask indicating an arrest I TESTSRCH 2 was employed to construct criminal histories for the candidates II on charge(s) 01-12, from 01 to 24 bi-annual intervals from 1969 through generated by TESTSRCH 1. Records were eliminated on county code and severity of 1980, inclusive. I arrest charge, i.e., only felony or misdemeanor events were considered. U 6. Wrote t~e records to a disk file.

I 4.2.2.3 Output I I U j Output of TEST 2 was a computer disk file sorted by search number and 10 I 13 U 14

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I \1 I , \ I' ,I 4.2.3.3 Output number. This file contained data from TEST 1 files and associated arrest II Output of TEST 3 is a disk file containing the data elements listed below: I histories. FIELD WORD/BITS 4.2.3 TESTSRCH 3 -- I I Word 0, Test 1 0: 47:48 TEST 3 correlated the records of TEST 2 with probabilities from the files Age Probability 1: 47:48 ( of APPENDIX 3, 4 and 5. These records then contained, for each search number, Sex Code Probability 2: 47:48 fingerprint score, probability factors and candidate identifying data (sex, year Arrest History Probability 3: 47:48 ( of birth and 10 number). 4.2.4 TESTSRCH 4 4.2.3.1 Input TESTSRCH 4 was the vehicle for I determinations. After benchmark determination, I Input to the program was the TEST 2 disk file TESTINPUT disk file and Avectors were input to the program by punched card. Hard copy was produced as probability files. [ described below. 4.2.3.2 Functional Description il 4.2.4.1 Input [ The program performed the following functions: ",11 Input to the program was: 1. Read each TEST 2 record. To A - vectors n: 2. Wrote the arrest history probability associated with each record to I 2. TEST 3 data files its work area. This operation may be interpreted as the programmatic [ 4.2.4.2 Functional Description equivalent of the following sentence: The candidate, with 10 number I The program performed the following functions: X, was arrested for the first time on charge C; q-time units prior fl u (t 1I 1. Read each TEST 3 record to the crime date in question. His/her probability of rearrest on the 2. Computed S2' S3 and S4 crime charge/crime date and crime place is p. The program locates "p" ~ ~ 3. Computed S in the probability file. 4 .. Wrote the Si' S and record identifiers to a disk file .1 .~:l m ,I I I . " "1 I 3. Determi ned the sex_ code probabi 1ity for each record s candi date s 5. Sorted the file by search number and S within search number age/sex and search numbers crime code. 6. Counted the number of records above and below the input record .' 'I I 4. Determined the age probability for each record's candidate's age, m 7. Computed the mean of the Si and S above and below the input record i sex, region and crime code. I ~ 8. Wrote to hard copy J; 5. Constructed a disk file, described below. I 6. Continued to TEST 2 exhaustion. H

16 1,5 n n / ~ ------~------~~ ~---,-,-----

,J -----==--=-"~----- I J II I 11 4.2.4.3 Output 5.0 Test Procedure I Sample output is given in APPENDIX 9. Output consisted of: I Recall th~t the test sample was denoted by n and the same target candidates 1. Sorted candidate list with ID numbers, the score factors and '0 _ Q ~I, were searched on finger 2, fingers 2 and 3 and fingers 2 through 4. Let l' I , total score. n 2' and n 3' be the one, two and three-finger search samples generated 2. The average value of the score factors, above and below the target I frOOl Q Search numbers 01 through 50 are assigned to Q l' 51-100 to candidate. I Q 2' and 101-150 to Q 3' I Let T = L tin T = L tin and T3= L, tin l ii,Ql 2 ie:n2 le: Q3 Clearly: T=T l+T2+T3. This is done to tabulate the search methods effectiveness I on one, two and 'three-finger searches separately. I The following procedures were executed: 1. SetAo = (1,0,0,0) and count the number of equiprobable candidates such that I Sl=O, by search number. Since the candidate mix agrees on fingerprint data and no other factors are considered, the expected mean list position '\ I of the target records is the list mid point. Consequently, ~T( AO ) was oj I I (: taken as benchmark. I; \ I , I 2. Initialize at some Al and compute: .1 "I) I • Tl ( A 1)' T2( A 1), T3( A 1)' TPl) II • (T( A O)-T( A 1)) I 11 • T( A 0) -T( A 1)) /~T ( A 0) I jl The Ti's are the sum of 1,2 and 3-finger searches. T( A 0) - T( ~ l} I is the difference between the benchmark value and the value at A l' The I last factor is simply the percent increase or decrease over the benchmark I value. I II 3. A 2 and successive vectors were generated after examination of TEST 4 .. ~ output. The social factors were varied one at a time until a local I j I minimum was found for each. The next factor was then varied in the I vicinity of that minimum. The process,continued until an apparent I 17 o I global minimum was noted. , 18 ,I " • ,; I, ~------

- ... -~~ I

... I I __ ._ ...... , ~ .. ' I - 4. Although the function was clearly periodic, this was empirically verified. A FINGER 2 FINGERS 2,3 FINGERS 2,3,4 TOTAL CHANGE % CHANGE I 5.1 Results 1,0,0,0 69956 5404 540 75900 -- The test results are given in TABLE 5. With A=(1, 12, .5, 3), T=24489. 1,1,1,2 28006 260.6 202 30814 7136 .1880 ! . 1,1,1,4 27492 2091 196 29716 8234 '.2170 I 1,1,1,6 26965 2010 176 29151 8799 .2319 ~T( AO )=37950; therefore, an increase of 35.47% over expected value was obtained. 1,1,1,12 27181 2129 186 29949 8001 .2108 It appeared that certain search numbers biased the overall performance. The 1,1,1,9 27099 2113 183 29415 8545 .. 2252 I 1,1,1,7.5 27021 2099 176 .29299 8721 .2280 test procedures were executed with search numbers 2, 3, 4, 44, 45, "46, 47 deleted. 1,1,1,6.5 26989 2048 174 29211 8739 , .2303 1,1,2,2 36548 3524 234 40342 ':'2392 -.0630 These search numbers correspond to charge categories Murder and Arson. 1,1,.5,2 26185 1a98 'l43 28226 9724 .2562 I 1,1,.25,2 26641 19,09 ·156 28706 9244 .2436 I 1,1,.75,2 26584 1910 ,144 In this case we computed ~T(Ao )=27228. The global minimum appeared to be 28638 ,9312 .2454 1,1,.5,3 25561 1949 136 27646 10304 .2715 I I at A =(1, 6, .5, 7.5). An improvement of 51.92% over expected value. Data for 1,1,.5,4 25873 1930 'i72 27975 9975 .2628 1,1,.5,6 26139 1997 :201 28337 e9613 .2533 these values of T appears in TABLE 6. 1,1,.5,7.5 26337 2Q15 198 28'550 ~900 .2977 I 1, 1, • 5 , 3 • 5, 25746 1939 :i77 27862 i0880 .2658 1,1,.2,7.5 ~69,83 2036 ,169 '8762 In application, th~ actual distribution of target candidate list positions 291138 .2309 1,1,.4,7.5 ,26908 1989 ,161 29055 8895 .2344 '- is a useful criterion for evaluation of a search algorithm's effectiVeness. The 1,2,.5,3 25005 1986 191 27192 10758 .2835 I 1,2.5,5,3 24456 1974 '186 26716 11234 .2960 distributions for T(l, 6, .5, 7.5) and T(l, 12, .5, 3) are given in TABLE 7. 1,3,.5,3 24480 1859 ,200 26539 11411 .3007 1 ,3.25, .,'5,3 24269 i977 201 26447 11503 .. 3031 I 5.2 Discussion 1;3.5,.5,3 24100 1983 224 26307 11643 .3068 1,4,.5,3 23838 1862 198 25896 72056 .3173 Abstract considerations of a search algorithm performance in themselves have 1,4.5,.5,3 22643 1967 208 25815, 12132 .3197 1,9,.5,3 22497 1860 230 24617 13333 .. 3513 I small impact in the operational environment. To the latent fingerprint examiner, 1,q".5,3 22677 1901 .199 2·4777 1'3173 .3471 1,12,.5,3 22296 194~ ,244 24489 13461 .3547 the IIbottom linell is simply: IIHow many identifications can we expect (at a fixed 1,11,.5,3 23074 2088 258 25420 12788 .3370 I 1,15,.5,3 22695 2018 249 24962 12988 .3422 I input level) using a system based on this algorithm?" For the manager, questions !~ 1 ,13.5,.5",3 22394 1972 '238 24624 13346 .3517 1 ,12.5,.5,3 ~~2314 1951 '245 24510 13340 :3542 of cost/benefits are germane, i.e., -IIWhat is the mean cost total per identification 1,12,.5,7.5 22401 1963 246 24510 14390 .3567 I . (for some fixed time interval) for the system? 'I ~ • J 5.2.1 Search System Environment '~ Some background material is necessary to reply to such questions. At DCJS, TABLE 5 III there is a mean discrepancy of at least magnitude 1 on ridge counts when the same 1Il ~ I rolled fingerprint impression is classified by different examiners or on impressions from the same subject at different times. The second condition appears e .. I~ I to hold even when the same examiner classifies the impressions at different times. 'I I:: I :' There is al~so a small but real error rate during data entry. It is, therefore, ,! .. " I 19 ,

,.

'---~<-"-~."....~.,..,.....-~--~--."-.~, .- I ------_. __ .------.------

, ~_,_,"~<~~.,.,.,.==-=:;:.-l: I I ~ FINGER 2' FINGERS 2,3 FINGERS 2,3,4 TOTAL CHANGE % CHANGE n '1 I T(1,12,.5,3) T(1,6,.5,7.5) 1,0,0,0 49094 4869 493 54456 - .- 15353 1746 " 1,1,1,2 157 17256 9972 .3662 LIST SIZE FINGER 2 l?INGERS 2,3 FINGERS 2,3,4 FINGER 2 1,1,1,4 14632 1140 , 128 16067 11161 .4099 FINGERS 2,3 FINGERS 2,3,4 I . : 1,1,1,6 14149 1280 117 15546 11682 .4290 1,1,1,12 14467 1188 155 15810 11418 .419.3 < 100 17 37 44 14 34 37 1,,1,1,9 14438 1042 164 15644 11584 .4254 200 5 4 6 I 1,1,1,7.5 14241 1079 163 15483 11795 .4314 3 1,1,1,6.5 14199 1278 .123 15520 11708 .4250 300 4 3 3 1,1,2,2 22183 2·341 '1,83 24.707 2521 .0926 1,1,.5,2 14084 1081 ::1;46 15311 11917 .4377 400 1 1 I 1,1,.25,2 . 14171 .1099 155 15425 11803 .4335 1,1,.75,2 14178 1109 154 154i41. 11787 .4329 500 3 4 1,1,.5,3 14059 . 1075. :146 15280 11948 :.4388 1,1,.5,4 13938 1038 '139 1'5115 12118 I .• 4449 1000 7 9 1,1,.5,6 ,-13829 ·1009 145 1'4'983 12.295 .4497 1,1,.5,7.5 13824 1012 :i35 14971 12253 .4502 1500 3 1 I 1,1,.5,3.5 1035 , '140 15],12 .~3937 12116 ,,4449 I I 1,1,.2,7.5 14648 1103 168 15919 11309 '.4153 2000 1 I 1,1,.4,7.5 14281 1103 '136 1.5520 11708 .4250 : 1,2,.5,3 13545 1.018 '137 14700 12528 .4601 2500 2 I 1,2.5,5,3 13280 1020 136 14436 12792 .4698 1,3,.5,3 13081 1027 141 1.4248 12980 .4767 3000 1 ,3.25,.5,3 12911 1028 149 14088 13190 .4826 ...... 1,3.5,.5,3 12746 1031 148 13925 13323 .4886 > 3000 1 ...... I 1,4,.5,3 12515 1039 156 13710 13718 .4965 1,4.5,.!?,3 12337 1038 160 1'3535 13693 .5029 TOTAL: 44 37 1,9,.5,3 12899 1097 .165 14111 13117 .4817 I 1,6,.5,3 12448 1041 .157 13645 13582 .4988 1,12,.5,3 12945 1058 163 14166 13062 " 4798 1,11,.5,3 13358 1119 :181 14658' 12570 .4616 1,15,.5,3 13148 1039 '151 14438 12890 .4734 I 1 ,13.5,.5,3 13049 1043 147 14239 12989 .4770 1 ,12.5,.5,3 13981 1037 145 14163 13065 .4798 1,6,.5,25 11884 .1046 161 13091 14137 . .5192 I 1,12,.5,25 12628 1218 • 195 14041 13187 .4843 1,9,.5,7.5 12185 1096 179 13459 13769 .5057 TABLE 7 I 1 ,7.:;,.5,25 12383 1072 170 13625 13603 .4996 ~ f

I\ I I I I TABLE 6 J I I i I I I I , I ~------

I FIGURE 2

I ,i unlikely that there will be exact agreement between the classification of a set 1:,_ I of impressions on two occasions. For an arrest print system, this presents III manageable difficulties if the aggregate difference is small. The algorithms

I employed are sufficiently insensitive to small discrepancies in ridge count to , .~ , 1 I' ·0 I produce a high-confidence candidate list of usable size. Unfortunately, the circumstances are not as favorable for latent impressions. I I The following factors are relevant: 1. Poor Quality Lifts I I The crime scene prints are often taken from rough~ moist or otherwise inhospitable surfaces. Ridge definition suffers accordingly. In such I I cases, a large ridge count tolerance may be desirable. I I 2. Print Fragments Portions of the pattern area may be missing or unusable. If the core I [ area is missing, a computer assisted search is not feasible. If, I'~-:,~ however, a sma 11 regi on about the delta is mi ss i ng, a good lower bound ) [ on ridge counts can sometimes be made. The print would be searched with '1\ [ a tolerance between the lower bound and 26. 3. Ambiguous Pattern Type I In some cases, the pattern type must be referenced, i.e., the print must ID be searched as two separate pattern types. I ~ Examples of both good and poor latent impressions are presented in FIGURE 2. Searches of some poor prints are desirable if there are sufficient minutia I U present to make an identification binding in court proceedings. The search algorithm, in shm't, should be able to search at least some poor prints. I U The difficulties inherent in using a large ridge tolerance are dependent I :\ on the portions of FPMASTER being searched. The largest single pattern type ~'; ~ , c' ;}' combination in the file is all ulnar loops. A ten-finger search in that portion 1-,- ;~ II ~It 20 'I~~ , , ' ill f ,

_v--.,, __... ~~~-- :1 . , .- I,..., lE I ( of the file, discriminating on pattern type alone, would output about 4% of the [ file. A single ulnar loop on any finger with a large ridge count tolerance 6.0 Evaluation Criteria [ would generally produce an even greater number of retrievals. The DCJS Rand D Bureau has adopted the mean total cost/identification as 5.2.2 Rationale for A quantitative criteria for system evaluation. This quantity has been chosen for 1 [ For the above reasons, A l' was tentatively assigned a value of 1. Post the following reasons: implementation testing may dictate a greater or lesser value, as appropriate. 1. It provides an estimate of what the agency "buys" for its monay. [ It appears, however, that a larger value would drive target records to unreachable 2. It produces an aggregate measure of diverse factors such as staffing portions of the output list. level, hardware cost and other dedicated agency resources. [ 3. It is readily employed into formula relating it to staffing level and system performance 1eve 1s (see SECT! ON 7). [ r.1anagement is, however, a 1so concerned with qual itati ve factors di scussed [ in SECTION 7.1 of this document. With the discussion of the difficulties inherent in the search process [ of SECTION 5.2 and the criteria of this section, we can now evaluate the potential use of the algorithm within a DCJS operating unit. [ I I [

I Ii ,:;i 11 21 22 j I n ,t 4'" 4, / ' / ------_.- - - -' - ---'----'-- , -'-~~_""-~"'

"y..,.~-:::,:,:,~-""",-~- / J / .. ' l ~ ;=---- I ,I. 11 constant for purpose of calculations. A graph of list size versus probability is given in FIGURE 3. Values were I The cost of secondary storage is not included since permanent system files I derived under idealized conditions in the following sense: 0ccupy a small amount of space. Estimated cost/year for computer usage is: 1. Exact ridge count data was input. , III [ 21 hrs/week x$150/hr x 52 weeks/yr = $163,800 2. Finger positions were known. [ In practice, management controls the working environment and C in the 3. Candidates were known to be on file. following ways: The actual probability will be lower. As a first approximation, the number [ 1. Set V by soliciting or declinifi9 submission from police agencies within of retrievals compared for a value of Pi will be multipl'ied by a factor of 6. New York State. Experience with the implemented system will determine the actual multiplier. EI, 2. By establishing the number of retrievals compared per case - or Management can select a value of L. The maximum work load can then be 2A. By establishing a desired probability of identification at established estimated. As an example, let ~=.3. Then, the number of retrievals compared D': confidence level for the main search program. per case is 50 x 6 = 300. [ Consider list containing 50z candidates such that z=l, 2, ... Mand where Data compiled at DCJS indicates the mean time to vi~ually compare two M is the integer such that all target candidates appear in a candidate list impressions is10 seconds. We can set the time for comparison of a fingerprint [ containing equal to or less than 50t candidates. I card to a latent impression at 2 minutes. In some cases, i.e., where the latent With each incremented list size (50, 100, 50M) associate the probability is of low quality, this will not hold and processing slows. It is useful, however, [ ~ as a first approximation. ;\ of identifying a target candidate fran a known sample. Vie assume the fo 11 owi ng: Let £11 = n { xix is a sample element corresponding to "Murder" or ~ ~ "Arson"} £1 I is a subset of the original sample, £1 • 1. Examiners work productively 6 hours per work day.

' J , ..' For £11, the probability of identification. of a target candidate after I 2. Three hours/day is spent comparing impressions. 1 I I i, An examiner can, therefore, be expected to complete 1.5 searches per work i. , : comparison of 50z candidates is given below. LIST LIST week. ~Ianagement then has three alternatives: LIST PROBABILITY-P i ~ I SIZE PROBABILITY-P ~ SIZE PROBABI LITY-P ~ SIZE 50 . 351 500 .703 950 .973 1. To set an upper bound on V consistent with current staffing . 100 .351 550 .730 1000 .973 .973 I V I 150 .432 600 .784 1050 2. To allow a backlog that \'/ill tend to grow if is approximately 200 .541 650 .811 1100 .973 .811 1150 .973 constant over time and greater than throughput capacity. ~ 0,~ 250 .568 700 .892 ' 1200 .973 300 .595 750 I , I I .892 1250 .973 3. To set the number of examiners consistent with chosen ~ and V. "'c' 350 .595n 800 I I ~ 400 .649'" 850 .912 1300 .973 450 .676 900 .912 1350 .973 An example will illustrate the third case. Let ~ =.3 and set V=275/year. 1400 1 i\ I I ", I , At DCJS, studies indicate an examiner will be productively employed 1350 hours/yr. correspondence by L. f'. Denote this m I 25 26 " I m I 't '~'."~r:;l~~~-~''''''-''''''''''''''~''-''~;:~-r.~~~~;:::~~~_ ...... "a::~ ... ~~= .. _A;~"~·~·,··,..,.::;:;;:::-:~::::7:::::::::::'--::::;;::'::~";:""'::;...~"--·'--' . .I .. ' .- ,

1. /,.------/ 0.

0.8

0. "

>­ I­...... J m m< o D:: D..

FIGURE 3 PROBABILITY Vs. LIST SIZE

- \ 0.2

0.1

i

180 580 780 I 880 11~0 I 1:&0 I 1340 I LIST SIZE l· {~::-:-~~==4-:<:::::::::::::·:;:-':::''''C::'':::::~::::'';:~:~:::':::~-::::::~:~:'~;':·::::::7'.'':.·.-;::--:::'.~::~::~M':"=:~7j:,;,·~·:,-:·",-::·!··~-=,...,r~Jt"~''<'''''''''''~'''''''''''''''''''''''''~~'''--''''''''~. -"r-'~':\--=''''''''''''''''=-'''~'''''''''''''"''''''''-'''''''''''""""'''~===:::'":K:>:'r~';:;l''.'t7."",,,,:::<:..~~c:~~,-~;::r-~~~.~''_'07~~~~~~~~.~_~ •....,.F;~..:::l''''~~''~'-..!::l~~rm..~~:i:!'~~~:,""'W"~.t';-,~~~ U

f /

. t, , , . " . [ [ Of this, 675 hours will be in the comparison process. Approximately 68 searches the output of the pilot system, a test made with the same parameters as [ can be completed per year per examiner. This necessitates four examiners for this in test search would yield an average of 2000 retrievals per search. [ value of V and ~. Under idealized circumstances, p;=.13 would require examination of 250 candidates If 50% of the suspects are on file, V=275 and Pi=.3, we can expect 1=137 x .3= I impressions. If the same Iilultiplier, .6, was used to account for a larger ridge [ 42 main search identifications/yr. Batch search identifications will contribute count tolerance and uncertainty of finger positions, the pilot system would require to the system tota"'s. At this time, we have little hard data, but based on .J I 1500 comparisons for a probability of identification of only 30% of the R&D .j I [ performance for the pilot system, we can estimate a minimum of 8 per year. System. Consequently, the value of C for the pilot system will be about five Cost calculations and data with fringe benefits at 30% of salaries follows: times that for the new system under the same input constraints .. • 0, ~".'l.i 1I : c 1 unit head (G-23) at $26,371 + .3 (26,371) = $ 34,282 The calculated value of C for the Rand D System is IIworse case. The IIbest

1 lirie supervisor (G-14) at $16,404 + .3 (16,404) = 21,325 case ll at B=.3 requires only 50 retrievals. V could be increased to 6 x 275 = 1620, 1 4 - examiners (G-12) at $14,681 + .3 (14,681) = 76,341 and I=H12 x .3 = 243 main search identifications per year. With 8 batch search 1 - clerk (G-3) at $9,144 + .3 (9,144) = 11 ,887 idents, C is less than $1500. TOTAL salaries and fringe benefits $ 143,835 Also, if Pr.10, as with the pilot system, there ~'1ill be correspon.ding decrease Floor space rented (850 ft2 x $6.61 ft2/yr) = $ 5,618 [ in the upper and lower bounds of C, assumed to be linear at this writing. Associated computer cost = 163,800 I C = $ 313,253 There are other factors to be included in evaluation of unit cost. The value [ and C ~ $7,349 (mean total cost/identification). of C generated must be interpreted in light of: [ Comparisons of the Rand D to pilot system must be done cautiously. 11 1. Identification with suspects and other service provided by the unit are There f4re twCJ "important factors when comparing the systems: :1 ~ not included in cost calculations above. 1. The Ij,t,! Jot system used its own sma 11 er data base. 2. The inclusion of computer related cost as above is suspect. DCJS pays '.," [ I. f' i'l rn 2. In i:11'1.V valid test of its performance, the average pilot system main a fixed annual fee for lease and maintenance of its computer system. E search target candidate 1ist"position will be at the mid-point of the l~ Within that fixed cost, resources are allocated in compliance with an list. Consequently, the number of retrievals must be examiner controlled established priority system. The latent programs are executed during be restricting parameters such as age, ridge cOLmt, sex or race within the evenings where usage is relatively low and, as such, do not normally ~ , , I the county or group of counties to be searched. The relationship between tax the system in terms of adverse impact on throughput time of other : ) the number of retrievals and probability of a target candidate appearance programs. The execution may be viewed as no real dedicated cost to the

is, therefore, not the same as that defi ned by L. ~Jith the agency. reasonable assumption that target candidates are randan1y distributed in Th~.above has provided some insight int6 projected cost assuming an 27 adequate work load, but-actual cost/i"dentificaj;ion must wait for data , 28 11

/ .- ;- '-_ ......

" .. ,-I [ I I I from the first year of operation. j 'i ' solution buys little. Neglecting computer cost, discussed above, staffing [ 7.1 Qualitative Interpretation 'j ,-> fIlI: levels would be comparable. At least an order of magnitude decrease in idents The formula of SECTION 7 gives a simple method of relating system performance [ could be expected and, as such, the value of C would rise markedly. to cost in terms of staffing levels. The "model" is, however, ~ilent on other jm C then is not to be viewed as a "make-or-break il value. Given the qualitative relevant issues including: [ :1 rn factors, the most effective computer assisted system available within the 'I. Interpretation of C within DCJS and the criminal justice community ';j L.t limitations of agency support hardware and procedures will be embraced. C is a in general. [ measure of ongoing efficiency. In this context, the Rand D System will provide I 2. Non-system related activities of the Special Services' Unit 1 ~ [ great improvement in terms of performance and cost to benefit ratio. Interpretation of C is qualitative, i.e., is dependent on management's u

" preception of the DCJS role in assisting line police agencies by means beyond ,! D such agency' s capacity and on a'l

~ [ certain types of crime, most notably Burglar~ by clearance through arrest. At the least, DCJS would maintain an individual with sufficient training to [ identify impressions against suspect arrest prints and to provide expert testimony

\ in court proceedings. Such an employee would not necessarily belong to a dedicated I latent identification unit. Since a relatively small percentage of employee time

\ : would be required, this may be considered a (near) zero cost solution. II I' () I It suffers from several deficiencies: 1. An impression cannot be identified without suspect.

~. 1 I 2. The perceived needs of the law enforcement canmunity and the public are o i ~ ), jo,r>:.<'

!.- \ l I not addressed. l The second alternative is a dedicated staff and a manual system. This u I 29 30 , I { C ) .. r-';;:~'e":!r::-;~-''"-'"~-~'''~'''"--'-''~- ~" ~~ -~--'~~~,:,,,,··,;,,,,·--"'~:::-7=~~;;xr.:-~---~ ... -:---~ ~'''~''->'''.'L'~'1~'...-:r-'''"\~~_~,:r..<,1_;..2:;.~.~~''=-~~'':::'-:;::'U'i;F;'\~-'<--~"- "'-~·--"--~~::::;:~--'::::::::::::::::-••::;;:':::::~.~"-'l~;::::::::::::!:T.~~>"~··" I ,', , f .- !', ------. I I 8.0 Implementation 5. Known target candidates will be searched with entered ridge counts I It is recommended that the new Rand D Latent System be phased into differing from file counts by one through four. This will enable production at DCJS. At the writing, portions of the system are currently in place more accurate estimation of system performance and determination of I and running parallel with the pilot system. Only the main search program is yet the procedures for best utilization of system output. to be i nsta 11 ed. I , 6 .. System documentation will be given to Technical Services. With the System development involved front-end determination of Functional Require­ system installed, maintenance is not an Rand D responsibility. The ments. Thereafter, administrative, clerical and operating procedures were I documentation is extensive and includes program and system specifi­ developed through an interactive process with Special Services' Unit staff members. cations and data file update procedures. Rand D staff will remain I The implementation process will be done within the scope of the documents resulting available to provide information as required. from this interaction. [ 7. The pilot system is removed. The tasks necessary for system implementation follow: [ 1. Install the main search (SEARCHER) program. Data entry is through CRT. Forms have been developed to document system usage. Rand D staff will [ provide instruction in data entry, program use and forms completion. As a result of this effort, procedures may change. Documentation will be 0 amended as required. 2. When Special Services' staff has been trained, cases will be selected "I i 0 from the unidentified latent file for main search. Cases will be entered and candidate lists generated. The unit supervisor will assign the lists ruH as time and resources permit.

j J if' [ 3. System functional requirements will be verified. It is likely that there will be minor discrepancies between the documented keystroke entry U 1, sequence and the actual product as well as in system displayed messages. , ; ,m . ' [ The documentation will be amended as required. 4. Searcher's on-line performance \'lill be documented by unit staff. Revision l. rm i; I Uj [, , to the estimates of SECTION 7 will be made if sufficient data is available. .. j U

I 31 u 32 I , ' · D I. ' ':r-'-'~... ~:.~""'-<-~<"- ···co ; ; :I ~~_._._._. ,. I . <_ •• -~-~-.-~~;;t'j :; / . l " /' I I 9.0 Enhancements data necessary to police agencies to obtain them. I The system provides an identification capacity not previously attained 9.3 Data Base Modifications at DCJS. With existing staff and funding, enhancements are possible that may The production latent fingerprint search program is, of necessity, cumbersome. I further improve performance and, accordingly, reduce cost. Its structure was dictated by the necessity of utilizing the DCJS master fingerprint I 9.1 Image Processing file and the CCH data base. As previously discussed, a major difficulty in the latent print process The fingerprint search is done prior to examination of CCH records. In the I at DCJS is the discrepancy between master-file and latent impressions ridge latter process, records are eliminated on location code, race code and search counts. Suggestions for improving accuracy of the arrest system are beyond the charges. Substantial process and I/O time are expended in examination of these I. ' I scope of this document. It is, however, possible to improve the ridge counting records. [ and pattern classification of latent impressions by use of the digital imaging Throughput time could be substantially reduced in two fashions: system installed at Rand D. 1. Inclusion of an arrest county indicator in the master fingerprint file - or [ An algorithm would be deve10ped'to enhance image contrast and IIc1ean Upll 2. Creation of a county index set for the CCH data base. blurred portions of the impression. This project is technically feasible but In the first case, the order of the search would remain unchanged (fingerprint [ would require substantial staff time. to CCH), while in the latter the CCH file would be accessed by county code and,

Once done, use of the R a~d D equipment could be scheduled for production therefore, the arrest fingerprint file by 10 number.

[ environment testing. If the cost/identification ratio was sufficiently improved, An in-depth dis.cussion i.s beyond th~ scope of this document and will not be [ management could be provided the data necessary for consideration of purchase given. The area is, however, wor~hy of exploration. ;- of production-oriented imaging equipment. ; . ~ 9.2 Soliciting Better Prints from. Police Agencies r' , It has been noted by Special Services' staff that the quality of submissions

,"":..1 U from line agencies varies greatly. Th~ latent print process has an unfavorable input to identification ratios and, as such, some police department administrations "~ ! U;( ; 1 believe their resources are better directed elsewhere. DCJS can enhance its identification capacity by the design and implementation ;1 t,1 ~~ ; ~ I; of a program to advise line agencies of our capacities and provide data relevant T ( l 0 to the evidence gathering process. 11 In effect, the agency can solicit computer searchable prints and provide the l'1 l 33 II 34 0 II / lj I I I I PART II 2. Data Requirements: The systems input data requirements are II1 III I ! also environment independent. With the latent fingerprint 10.0 Application of the Documentation in Other Environments system, for example, the acquiring agency must have or develop

I The information contained in the following sections is intended to assist ~ a com~uterizec! crimina'l history system to 'store data elements I potential system developers in the following areas: ill including offender's reported age, sex and facts concerning 1. Utilization of DCJS documentation. arrest. The specific mechanism by which the data is acquired [ 2. Formulation of a development plan and suggestions for unit staffing. and stored is irrelevant at this level. 10.1 Comments on Technology Transfer' 3. Program Logic: Program logic can be categorized as environ­ [ On initial examination, technology transfer of a computer system is straight­ ment dependent or environment neutral. Environment dependent forward. System documentation, or in some cases source code developed in one [ logic is used to formulate the interrelations of internal data environment, is implemented in another. In general, the environments may differ elements, file structures and sequence of operatio~s specific [ in system hardware, operating systems, data base management systems, available to the program language. programming languages or, in less obvious instances, operating procedures. [ The end result is that code developed on one system can seldom be run with- Neutral logic is, itself, of two forms. It is a code independent out modification on another. description of computational methods expressed in natural language [ Clearly, similar considerations apply to program documentation. or mathematical symbolism, or a natural language description of 10.1.1 Transfer Categories the relation of the program's data elements. [ Transfer of a computer system can be broadly grouped into three categories: -, 10.1.1.1 Design The latent fingerprint system is based on a method of ranking [ ~ The system possesses a logical structure. In this context, by Illogical ,j , persons with an arrest history on the basis of their reported ll ii [ structure is meant: physical and behavioral characteristics. The algorithm is : . ,-, ! ' 1. Functional Goals or Requirements: The formally stated system purpose given both as incorporated into DCJS code and in abstract form. i: I' i) [ and applications can be expressed independently of both the system ~ The latter version would be employed by an acquiring agency. 1, hardware and software environment. The acquiring agency can, of j 10.1.1.2 Functional Components j ,1 [ I course, define their own specific objectives to supplement those 1 ~ This transfer level requires similar hardware and utilization of the

; i u provided. Fundamental changes, however, may require alterations ~ same complier/language combination. Existing system specifications can be used , : to system design. for file design. With small revisions, programs are also directly codable from , , .", " ! 1 speci fi cat ions . i U 35 36 U -\III til _.- '·-:;7;.!.-r-·-""--·"-·-··~ ,.- ~ .. I I I 10.1.1.3 Turn-key Systems At this transfer level, source code is re-compiled and executed without impressions. Other agencies may use minutia based storage and I modification. In effect, a system is designed for a specific operating environ­ search systems. If so, the general method employed by DCJS could ment. In the private sector, business systems can be designed in this fashion be used, i.e., ranking candidates on arrest characteristics, but I and implemented on a specific computer system. In government, a system norma11y the fingerprint ranking procedure would have to be reformulated. exists within a larger environment, i.e., is one of many running at an installation. The potential system developer has been provided exhaustive documentation, I Under the conditions implicit in technology transfer, turn-key system exact statements of data required, and mathematical procedures employed in system [ can seldom, if ever, be realized. development. Sufficient data is presented to initiate a sound program, but only 10.1.2 Latent System Transfer Catego!l 1ev~1 one transfer is attained. [ The Latent Fingerprint Identification System utilizes the resources 10.2 Staffing a Latent Fingerprint Identification Unit available at DCJS. There are several system components that will probably not Currently available technology does no t perm,'t ma c hine comparison of latent be found in other environments. They include: impressions to arrest fingerprints in the production environment. The human r 1. Computerized Criminal History Records: The system employs the examiner remains central to such operation. At DCJS, the special skills and L agency's extensive criminal history data in two ways: dedication of these employees has been recognized. The agency has also formalized • Formulation of static data files: These files contain probabilities the Latent Identification Unit's structure and procedures. discussed in the system report, which quantify criminal behavior m 10.2.1 Role of the Latent Fingerprint Examiner The latent fingerprint examiner has been assigned Salary Grade 12 by the patterns. The data relates to events occurring within New York 1 'I rn. State. Data derived in other portions of the United States may !Jj New York State Department of Civil Service. Examiners engaged in processing arrest differ to the extent that the same performance cannot be realized. fingerprints are assigned to Salary Grade 9 . In this fashion, DCJS has made the In any case, the acquiring agency must conduct statistically valid 00 acquisition of skills necessary for specialized \'lOrk financially rewarding. i l i U tests similar to those done at DCJS. Within the Civil SerVice System, tests are given to establish eligibility

~c:>. < ~ ll for appointment. Cop,'es o'f the examiner's job duties, prepared by the Department f • Creation of "search records : The search program accesses our data base during execution. Records are formatted for use by the fJ of Civil Service, and an exam,'nation announcement are given in APPENDIX 10. ! I scoring algorithm. The information on possible candidates must 10.2.2 Unit Role and Structure be present and, th~refore, files of similar content are required. U The Special Services Unit performs the following functions: 1. Within the context of administrative procedures, categorizes latent I' I 2. Fingerprint System and Files: DCJS utilizes fingerprint pattern , ' fingerprint impressions received from line police agencies. , I type and ridge count (for loops and whorls) to differentiate fingerprint : , 37 I 38 "

. " ... -/ =-=---...-.

I '-'1 1 I I 2. Inputs data to the computerized identification system. 'II 3. Determination of staffing requirements. I 3. Compares arrest fingerprint cards identified by the system to latent /1 4. Analysis of hardware and software requirements. impressions. 5. Formulation of system specification . ( ,~ tI 4. Effects identification of latent impressions against suspect prints . '-.! II 6. Scoring algorithm formulation and testing. supplied by line agencies. , ft [ !\I 7. Program documentation. 5. Attempts identification of cadavers by means of agency fingerprint , J 8. Program coding and testing with update of documentation. [ systems. ,II 9. Implementation and p~st-implementation testing. 6. On request, the unit supervisor provides expert testimony in court The management and analytic skills prerequisi~to the project are assumed. [ proceedings. I 'J "I ~ It is also assumed that the system is to be developed in an environment where an 7. The clerical and-administrative procedures mand~ted by 1 through 6, r:. ',I ~ arrest fingerprint system exists or is under development and that criminal history u above. data is available as discussed below. The unit consists of a unit supervisor, line supervisor, four examiners, [ 10.3.1 Analysis of System Data Requirements and a single support clerk. Estimates given in the system report indicate that The search program requires the criminal history recor.c{ containing offender1s: [ this staffing level is sufficient to process 1000 cases per year with a 30% chance • sex of identifying a suspect on fi 1e. Of ·the 1000 cases input per year, an estimated • race [ 25% are computer searchable.

The estim~tes will vary in other environments as a function of the size of • criminal history (charge, place and date) • fingerprint data U the master file(s) searched. Results should be outstanding if the search population There are four obvious storage requirements for this data: is small, i.e., under 250,000. In any case, the developing agency can estimqte cost :1 , E 1. It must be stored on random access devices such as disk. to performance relationship using the model presented in PART 1 of this report. 2. It must be updated regularly, preferably in on-line fashion. li 10.3 Steps Towards a Development Plan for a Latent Identification System , , E:oJ 3. Storage system software must link or otherwise correlate all arrest II1 i The formulation of a Uatent Fingerprint Identification System, based on DCJS it ;1 I! .1 events for an offender. r.!J descriptors, can be categorized into nine major components. They are: , , Criminal history records are also used to create static data files. This ,"", 1. Analysis of system data requirements. ~\ [J date requirement imposes another condition: 0 2. Formulation of functional requirements, performance goals and 4. Criminal history data is available in sufficient quantity and ., objective function. 0 fJ duration to allow valid canputation of the probability f'ile elements. . , :1 U 39 o U n 40 , / --==~--.. ,. I II I II The fingerprint data requirements are not as severe. S, can be formulated 10.3.3 Staffing Requirements I in terms of operation on descriptors other than ridge count. If an agency, for Staffing requir.ements are contingent on diverse factors including: I example, employs a minutia based system, development could proceed in the fashion 1. Estimated work load, i.e., number of cases per unit time. evolved at DCJS. The relative weight of the factors and program operating 2. System performance in terms of identification probability per [ characteristics would, however, vary considerably. unit number of retrievals compared. 10.3.2 Functional Requirements and Objective Functions ',III 3. Non-system related function performed by unit staff. [ :'! II After analysis of available data, a decision to initiate the project · I 11 4. r·1anagement selected confi dence 1eve 1 . · 1,1 can be made consistent with the developer's operating and political requirements. 5. The characteristics of the ~omparison prqcess, i.e., comparison [ f! il Once made, initial functional requirements and system evaluation criteria must be • of actual'arrest cards. to .latent fingerprints or one of the IT formulated. automated image retrieval systems currently available. 10.3.2.1 Functional Requirements I Factors 1 through 4 are discussed in SECTION 7 of this report. The method [ By a system's functional requirements is meant: ,) I of image retrieval is not, but is critical to the latent operation. Studies 1. The set of operations to be performed by the system programs. done at DCJS indicate a digital image storage and retrieval system would improve r:0 2. The human/system interface including all data entry procedures. the comparison time by at least an order of magnitude. The examiner's work load ·1 I 3. Output requirements. could be increased accordingly. [ · I '.. 11'I . At DCJS, a' preliminary draft of system requirements was prepared in 'J An agency dedicated to efficient latent print processing should explore the [ conjunction with Special Services snort1y after project initiation. This I. cost and benefits of the various automated systems available. document and its many revisions were employed to provide guidelines in preparation 10.3.4 Hardware/Software Requirements j) [ The developing agency must assess the impact of running a latent system of system and program specifications. A copy is included in the documentation I I package submitted with this report. 1 on available resources. At DCJS, several hundred hours of computer time were riii. 10.3.2.2 Evaluation Criteria .'.j I expended in data extraction and analysis. The search programs routinely examine " hundreds of thousands of fingerprint and criminal history records. The actual I It is suggested that the evaluation criteria of SECTION 6 of this report ','1 be adopted by system developers. At the outset, however, it may be difficult to -I I expenditure of computer related resources is, of course, dependent on the system's "" ."., ~ assign a specific monetary value to an identification. The object'ive function, final configuration, the hardware on which it is implemented and competition with I I i. rather, is to be interpreted as a measure of relative performance between systems other agency systems. or as a measure of efficiency. 10.5.5 Formulation of System Specifications I I -' ; Implicit in this portion of system formulation is a task plan for development , . 1· I [ 41 42 I

• '" I. /' I I of the component programs. Such procedures can enhance the efficiency of the LIST OF APPENDICES ( development plan. For example, we recognized that the system programs to compare new arrest records to unsolved cases could be designed and implemented in a [ 1. SU~1NARY OF DCJS FINGERPRINT DESCRI PTORS relatively short time. They were, consequently, installed first. The debugging 2. DESCRIPTION OF ALGORITHr·1 RH [ and test process ran concurrently with the data extraction.and analysis programs. 3. SELECTED GRAPHS OF AGE FREQUENCY DISTRIBUTIO:~S 10.3.6 Scoring Algorithm Formulation 4. SELECTED GRAPHS OF SEX CODE MIX [ Procedures are given in SECTIONS 4 and 5 of this report. 5. ,SELECTED GRAPHS OF ARREST HISTORY 10.3.7 - 10.3.8 Documentation and Coding 6. PROBABILITY FILE CONTENTS [ Program coding and documentation are, ideally, an interactive I 7. TEST SAf.1PLE COf.lPOSITION r[ process. Since update of data, files and algorithm weights are periodically I 8. TEST SAt~PLE FINGERPRINT DATA necessary and it is essential that documentation be thorough and reflect coding. 9. TEST SEARCH 4 HARD COpy

'I ;1 I E 1 10. EXAMINER'S JOB DESCRIPTION

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I I FINGERPRINT CLASSIFICATION APPENDIX 1 TRADITIONAL HENRY -TYPE DESCRI PTORS I SUr4~1ARY OF DCJS FINGERPRINT DESCRIPTORS 1. Pattern Type ,1 I ( The most basic descriptor is the pattern type assigned to each print. Pattern rIl types are divided into three major groups: Arches, loops and whorls. Each of [ lJl these groups is broken down into subgroups. These subgroups are: A. Radial and ulnar loops B. Central pocket loop whorl, plain whorl, double loop whorl and [ J rn accidental whorl

, ~ C. Plain arch and tented arch [ The pattern type is determined by the presence and location of the core and delta(s) on the fingerprint impression. This applies to loops and whorls ,', ~ [11 only as the arch group, with several exceptions in the tented arch subgroup, is characterized by the absence of the core and delta(s). The rules, employed by the DCJS operating system for locating core and delta points, [ are contained in the "DCJS Fingerprint Identification Manual" prepared in ~979. Copies are available upon request. [ II. ' Ridge Count 1 The ridge count is the numerical value assigned to loop and whorl pattern [ '1 types. To obtain the ridge count, the core and the delta(s) must be located. :1 ~ After these points are located, a straight line is drawn between them. The number of ridges crossed between these two points are counted amd assigned to . , I " I each pattern. , I "l I I o It I' t; I j I ; "3 " i1 i I '.e I I I I , '\) '"

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I .1 A2-1 APPENDIX 2 $1' the fingerprint score factor of the latent search algorithm, was I DESCRIPTION OF ALGORITHM RH I adapted from one developed by Rand D statistician, Robert Hall. Mr. Hall1s I I research had a two-fold purpose. 1. To reduce the number of misses on lO-finger searches and I 2. To reduce or eliminate dependence on information provided by ( arrestees such as date of birth and initials. The algorithm is based on the mathematical concept of a metric or distance

[ , I function. Given a pattern type combination, the 10 ridge counts, if available, ,~ I are considered to be points in a 10-dimensional space. The input data provides [ I one IIpointll and the elements of FPMASTER within the input pattern type a set of [ points to be compared. In terms of the metric used, the closer the points, the ~ more likely a match. There are several complicating factors: , I~ 1. Arches and tented arches have no ri dge counts. ~!hen these pattern types are present, their contribution to the total distance must be comparable ill to that for loops and whorls.

2. If the search card or a FPMASTER record or both have ridge count data I rn nI' fJ missing, an empirically determined value must be substituted. rn The weighted sum of ridge count differences is a simple distance function 11 11 II" that satisfies the desired characteristics. Testing was done and, although f " u time and physical resources prohibited accounting for all conditions that occur, ~ u yielded excellent results. '! ~

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.20 T, ,I "- , AGE FREQUENCY or STR I BUT ION .18 t PROBABILITY OF ARREST FOR BURGLARY I ,I UPSTATE MALES

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.. 20 AGE FREQUENCY DISTRIBUTION .. 18 PROBABILITY OF ARREST FOR ROBBERY DOWNSTATE MALES

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APPENDIX 4 SELECTED GRAPHS OF SEX CODE MIX

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0.8 l!, ", I 0.7 PROBABILITY OF ARREST FOR BURGLARY UPSTATE MALES FEMALES ______0.6

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.10 PROBABILITY OF RE-ARREST FOR ROBBERY IN 6 MONTH INTERVALS .09 FROM DATE OF FIRST ARREST UPSTATE MALES .08

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.10 PROBABILITY OF RE-ARREST FOR BURGLARY IN 8 MONTH INTERVALS FROM DATE OF FIRST ARREST UPSTATE MALES

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, .' . , '- I I APPENDIX 6 A6-1 I PROBABILITY FILE CONTENTS ( PO/LTN/PROBAG PROBAG contains age frequency distributions for both sexes, 2 regions and [ the 12 latent charge codes.

[ File Specifications

Number of Records: 48 D R2cord Length: 50 words lJ Format: Record 0: Males, charge code 01, upstate I: Record 1: Males, charge code 01, downstate i' .; 0 Record 23: Males, charge code 01, downstate 0! 24: ;1 Record Females, charge code 01, upstate Ut ~

) Record 47: Females, charge code 12, downstate ~ E , Record Content: Real numbers in (0,1) [ Word 0: Age 16

0 ~ vJord 1 : Age 17 r, U Word n: Age (n + 16) '" ID ~ ~~ . [ v'

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I I A6-6 O.CGOO 0.00[,0 D.GOOO O.OOCO C.COCO C.GODO G.CiCCO O.tooo D.OGOO O.OGCO I O.GOGO D.OOuO O.wOOO o.oooa O.iJGGC" C.GGOO 0.0000 0.0000 (.0000 o.eccc

0.C742 C.0983, O.lu30 0.0941 C.C9C7 (.G835 0.(686 C.. C.619 C.0538 0.03:30 O.(i330 0.1.1330 u.C330 c. (d 3J C.(;122 (.0122 0.(122 0.(122 G.0122 0.oe42 I O.~C42 0.U042 L.CU42 D.OC4.:! L.CO~o C.GC26 G.0026 (i.(Cl6 ('.C026 0.0013 o. LO 15 0.0013 u.GG13 u.0013 O.UCC6 (.GGC6 G.G006 O.(GC6 0.(;006 C.OOC3 PO/LTN/PROBSX [ O.(;OG3 G.uOO3 O.Cw03 C.GOC3 C.OUU3 l.GOC3 L.C.OC3 C.COG3 C.(003 o.ceco PROBSX data elements are probabilities of a candidate at each age from 16 0.0351 0.0466 C.0!>71 0.0579 (:,0589 (.0645 0.0733 0.(702 C.0666 O.04l:4 0.0464 0.0464 0.0464 O.C464 l.02G6 (.0206 C.li2C6 0.(206 0.02C6 0.01C3 through 65 years, of either sex and upstate or in New York City being arrested [ 0.(,103 C,.010j (,0.(;103 ('.C1(;3 (.C05~ l.e.u)/:! c.uO)t 00(058 c.oe58 C.OC~5 0.(035 0.LJO.55 L.CQ55 O.Ou3) G.(C12 (.GClC: C.GU1Z C.C012 C.GC1~ C.CCC8 0 .. 0008 C.GuO~ O.CLQB 0.00(8 C'.DOLl C.OCU1 G.uG01 O.(COl G.cee1 0.GOC5 . on any of the twelve latent system charge classes. "Downstate" is county codes 23, 30 62 40 and 42 "Upstate" con 't f th h .r " • SlS S 0 e ot er 57 state county codes 0.C500 0.0482 0.0609 0.0584 0.066G (.0635 C.GS)S 0.(533 0.C4C6 0.035C { 0.C350 O.(3)G 0.(,350 o. Cb ~o l.02C::(I C.l~23 (;.('22b C.C22a (.(228 C.0137 ~. as specified in TABLE 1. System charge codes are defined in TABLE 3 . C.(,137 (;.G137 C.C1')7 0.0137 L.Q1.H (.0137 O.L137 C.L137 (,.(137 0.0086 C. (0 86 U.OU80 \.J.LG~6 G.0086 u.OL25 l.u(ZS 0.(02) C.CC25 C.CC25 0.(;C20 I II 0.(020 G.OCl2u 0.L02G G.G020 C.0010 (.G(;10 0.0010 0.t010 U.CG1G 0.0051 t ' File SpeCifications [ 0.0390 0.C390 0.0437 O.u516 ('.0519 (.0489 G.050e 0.(519 C.C456 0.03<;8 0.0398 (j.L39~ O.ld

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I 0.1249 0.1~18 C.13SI!) O.137!'3 (.1::>86 C.173C C.1753 O.16f.2 (;.19t5 File Specifications 0.1611 0.1/02 O~16bU C.l,o( (.1461 1:.1326 C.lo33 (.1643 G.14U 0.1753 U.tb44 C.17';') (.14(4 (.22l2 (.lt48 C.195e C.2234 C.18:32 0.1690 G.2:i29 O.1~i:.7 L.1ci39 (.t!LLO l.16tl C.t:eOU t.176) G.2857 Number of Records: 48 (,.3462 U.1176 L.34i::i u.:>uC8 (..0714 0 •.H5: C .. 1333 (.. • 4 (.(; C 0.3019 I Record Length: 240 words

0.(759 0.0645 L .. L9C2 O.{;~j2 (,.1((6 (.0';47 0.1197 0.1310 G.1232 C.11:35 Record Format: Record 0: Male, charge code 01, upstate I G.123C: 0.1.>11 C. 12(, 7 0.1(:40 L..145o (.1)~4 t...lot6 G.1617 C.1431 O.14~9 0.1SJC, 0.1,;95.5 J.1.H2 0.1662 [: • 1 41 S (,.1468 0.1271 ('.(,979 u.1L67 O.1~

(.2363 C.2160 <.:.2541 C, • .c4C2 ['.2253 C.2119 (.~2b4 (;~4::222 (.2(73 C.2645 Om 1 n) O.191b L.13L~ (.lS7I (.1ies C.1.c90 0.1 C 94 C.12~2 (.(.816 G.13Cl 0.1'113 0.1402 0.1171 G.Co7Y ('.C741 (.164'1 L.2CGO 0.1463 c.. C1,4 C.C847 I '~r r Record 23: ~1ale, charge code 12, downstate (.,.0769 0.(,':')0 0.1538 C . 0_ '- i.J 1...1bbl ( • '1 154 t.125(; C.2(~L C 1 J. ? lJ C.C5CC C.C'tC9 0.20UO C.Cl,;(O O. B Goe t.2i..CQ L.CC00 C.LCl~ C,.CLCC c:ccc6 (.25(0 I Record 24: Female, charge code 01, upstate I Record 47: Female, charge code 12, downstate Record Contents: Real numbers within (0,1) I Word 0: First interval, subsequent charge code 01 ,

Word 11 : First interval, subsequent charge code 12 "..

Word 239: Twentieth interval, subsequent charge code 12 File Contents: Real numbers in (0,1) ,

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~ . - . . , / .. ' .- i j '~'I ,I , , '\ [I A7-1 : tt< 1 I I ! I, I TEST SAMPLE r II j - , , NO. OF PRIOR I : SEARCH NYSID # COUNTY CODE LATENT CHARGE SEX YOB ARRE:STS APPENDIX 7 I l f" * !, 01 Lost duril g programming I TEST SA~1PLE COMPOSITION i , j 02 4435029 23 Murder M 1935 2 I i i I 03 4442726 30 l>1urder M 1954 4 I 'I ,I ,\ ; I , 04 4444237 30 Murder M 1962 2

! ~i i I I 05 4420168 23 Weapons M 1963 2 . jj' ! " I j 06 Lost duri g programming I , I ,I 07 " " " ,: I .' Ii 08 4424319 23 Weapons M 1963 2 I , I .-.' I 09 4425035 23 WeaP9ns :M 1961 2 I j : '. I " , .- ,.' j I 10 4421428 23, Assault M 1962 3 I - '. , I 1 i 1 11 4424155 40 Ass'au1t 1£ 1911 2 j ..

i ! [ I 12 4424163 40·'f .... Assault M 1963 2 ! ~ , I . ~ i I , .. ~ I 13 .4425269 33 Assault M 1962 3 ,I . , ( ~ , 14 4426141 59 Assault M 1962 8 m I, , I 15 4423363 23 Drugs M 1953 3 I ! i ~ I 16 4425788 30 Drugs F 1962 1 ! 17 .4427431 23 Drugs M 1959 2 .\ i • ~ Drugs M 1944 8 ; 18 4431209 30 I ! 19 4431322 40 Drugs M 1962 4 : U i , 20 3830321 30 Forgery F 1942 1

1 I j 21 3845752 55 Forgery M 1951 4 U ! 22 4225519 53 Forgery M 1958 4 I 1 D i 23 4422334 51 Forgery F 1951 2 I I 24 4424212 40 Forgery M 1959 3 I ." " 0 : 25 4420447 23 Robbery M 1963 3 . [] . , .

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I TEST SAMPLE t1, ~ \ NO. OF PRIOR I SEARCH # NYSID # COUNTY CODE LATENT CHARGE SEX YOB ARRESTS 1 ! ~

t ; 26 4420567 23 Robbery M 1960 2 I .. '../" I , i 27 4424940 62 Robbery M 1960 5 .: 28 4420972 40 Larceny M 1962 2 I I I r I I 29 4420981 23 Larceny M 1958 3 f I I t I ! 30 4421438 30 Larceny M 1947 11 If-~' , 31 4422114 23 Larceny M 1961 1 tr .; I1 I rg I 32 4436548 62 Rape/Sodomy M 1962 2 l', ;I l'i I 33 4439645 62 Rape M 1~60 3 t I 34 4440324 23 Rape M 1963 1 ~l 1949 6 t I I 35 4441310 55 Rape M i" 1 36 4421375 30 Burglary M' 1962 2 J I 37 4422249 35 Burglary M 1963 2 38 4422515 23 Burglary M 1963 4

I 39 4423966 57 Burglary M 1963 4 I 40 4423007 33 Mischief M 1961 3

'l" 41 4427426 40 Mischief M 1962 3 ."

J 42 4429452 23 Mischief M 1955 ,5 I i , ',j ! 43 4430587 40 Mischief M 1962 1 . I 44 4072321 30 Arson M 1952 5 : l :: 45 4075158 23 Arson M 1957 9 ~ -! \. -- . f I 4 .,. 46 4428570 23 Arson M 1963 .j ,\ . 47 4420977 30 Arson F 1959 6 I I I 48 4421438 30 Auto Theft. M 1940 11 J ; i 1 \ Theft. M 1962 8 I. I I 49 4424823 30 Auto ~~ ~ ( ' { / 1 . / - 50 4427019 14 Auto Theft • M 1960 3 . f ,'1 ! " . , , I ;}! .'" . 1 , t I ~: .' ,. , , , , \ n ", ~" __ "._...... ,....>«.,,. •. __ ,c • .-" .,~ ...,. ,. -.~ ,-., ... - .. ,-~~=::~~--..:~~c;::,;...~:..~~-;!;:"~=~--'--~' , - =r·.~"-""-·~U' ,• . <'. ~ I I " l • 1 .-

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ilAfE 1-JJ-dl ~TAT~ OF N~w YORK i RAt! {~O f l 2.0·' TlI"'~ g9 I 5 Dl~'S10N Of CRL~LNAL JUSTtCE SER~LCES ,J JoG J'.i FAX NO CO~fl0E~T'AL Tu: DCJS • PHASf III DOS 01-21-62 RAt BLACK SEX ('.ALE HliT S-O.5 •• _ •• __ w __ •• *_ •••• ___ ._~~ •• ~ __ •• y_.w. •• , ..... _ ft ...... -- .. __ SOC I NAM~ INY~LRY,O~L1 I I NYU!> fBI ...... ' ...... _ ..... WI_ ...,...... _ ...... &6 .... __

NA~tS ~SED BY SUBJECT Di 10 01 05 01 00 .8.~ 8~ 83 03 01 00

_._ ...... _ ...... 'IiII ...... __ ...... -. "" .. ' ...... n ...... ""' ••••, ...... : ...... _ ...... - .. < < < < < < < 'RI~lNAL HISIOKY > > > > > > > _ ...... _____ ...... _._ ..... _ ...... _ ... ,. ... ___ ...... r. .. _.,.~_ .. _____ ••_ .... I A~K~ST PAT~ I AK"~ST CHARGES I ~ISPOSIriON ANO I I I~fg~MArIO~ CO~kECTIONS DATA __ .. _.~~~~ .. u_'_ .... _.. ~* •• _~_._~ •• ~."~~~.w_.~.~_._M_~.~~~ ... ~_.~.N.~P_~_~_~."._.~ ARR uT/PL O~·~6-7~ ATT~MPT~J ~~"GLARV~3 - • 01SFOSITIO~ - - 06lJ678 01 KINGS COUNT( 11J/14D.~a PL CLASS E fEL U6·06-7d tRIM tRT kINGS 9250011< PL 14020QOOFA3 060678 230000AZ hCIC Z2~9 D~T 060678 CkM JT/PL J~·Ol.7d COhVICTED UPON PLEA Of G~lLTY 8"07 ·~o 0&0 ~c\0 01 102 MT-0281 K1NuS COUNTY , PUS~cS~lON uf HUH~LAR TO~LS THE FULLOwlNG CHAHG~(S): 92.50;)11< 02 14J.l~ PL '~ASS A MLSO UI~Ort~tkLY CON~UCT OOOU. 0 PL 1403S0QAl'IOO OS PL 240 zeooa vOO ARR "/AGY NCIt Z,06 '4C.'U PL tLAS~ VIOL NYC?O PCT U9~ SEhT: ~J/5~ fINE PAID 010090Q 19 CRl~1NAL M!SCHIEf - 4 1N FOLL ~ATISfACTION Of: 0.3 905 C~T CON# 14~.JO PL CLASS A MIS~ POSS d~Kli TOOLS PL 14S000QAI'IC4 02 PI,. 1403500AI';00 Ntle 29J9 '4~.j~ PL CLASS A ~ISO fAX NO C~I~ MISCHIEf-4 03 PL 1450000A ,~4 145.JC PL CLASS A ~ISD CItIM TRESPASS-.5 04 PL 11001000 Bpt(l3 14G.1l PL CLASS B "'ISO :..

08-29-73 ('RIM CRT KINGS 2300(lOAi. (, C60678 RETURN ON WARkANT u2 02.2 'IT-0248 i . 1 03 M Z3-7d CRIM CRT lUNGS 2.30000AZ 060678 Rt:1URN ON WARRANT 03 022 1'1 T"O 242 Ii~ {; Od-2j-7~ r.RU\ CRT KINGS 2300r,OAZ ; 060678 K dENtH iiAl\H~NT ISSUED C4 020 "'T-D242 ~; ~ - '.' ,I ,1 Jd-OY-78 CRI.M CRT kiNGS 2300(lOAZ C!6·J678 \1 RCTURN O~ wAIl/UNT CS 02.2. HT-0227 ~ .. , I \, I Ud y C9-7d CR!M CRT ~lNGS 2.500(10AZ. 6 (; I 060678 U£NCH WAR~ANT ISSU~~ 06 02.0 I'IT-02.21 7 (\ • i 07-11-78 CRIM r.RT I

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O:;/~':'~/8:l 1-1 08::1"8--'--0 08;;18 SNPS'O'J8 I J V 4435029Q M 04/07/35 I H APPENDIX 8 TOO;W06~U12;Y16~W15.AOO;W10,U11,Wle.W14. j 44350898 04/07/35 ' ,I V M 1 H TEST SAt~PLE FINGERPRINT DATA AOO;W06.U12,W16.W15,AOO,W10.U11~Wla.W14. '0 I I 06/23/81 1-I 08~19 T-O 08~19 SMP2078 " ( V 444e7~6P M 01/02/54 VEe i U18;W15.U12,weo.W19,TOO,W15.W20.W19,U18. I I V 4442726P M 01/02/54 VES r U18;W15.U12,W20,W19.AOO,W15,W20,W19,U18. I V 444~726P M 01/02/54 VES I I I U18,W1S;U1~.W80?U19.TOO,W15,W80,W19,U18. ( V 4442726P M 01/0!/54 VES U1B,W15~U12~W20.U19~AOO,W15,W20;W19,U1B. 06/83/81 I-I 08:.19 1-0 08;19 SNP2078 1 I I) 44'1'4237P. 11 01/19/62 P C I ( " U~1;U05.TOO.U04,U06,U18;P'03,U02,U04;U13. V 4444237R H 01/19/62 P C 'JI ;j I ( U21,U05~TOO,U04,U06,U18,P'03,TOO,U04,U13. I .J 06/83/81 T-I 09;19 1-0 08;19 SHP8078 v 4~eOi68H M 03/13/63 JJD :,1 ( W2i,W141W187W20~we1.W!.1,W18,W16,W19,U16. I , , I IJ 4'1'20168H N 03/13/63 JJD W21,W14:rW18,W80,W81,W?1,W12,W16;U19,Ulb.

o , ( 06/83/81 I-I 08;19 1-0 08:.19 SMP2078 ~ V 442~319L H 03/07/63 W R I U15,U13,U15,U13,U17;U13,U06,U02,U14,U15. . (;) ( 06/23/81 1-1 08;19 1-0 08~19 SMP2078 ',j V 442503SZ M 04/09/61 W P I U13,U12,W13,W16.U15,U15,U12,U14,W16,U13. I ·1 , (- 06/23/81 1-1 08=19 1-0 08~19 SHP807S V 44214889 M 05/11/62 DAM ( I U19,Y15,U2i7W80:rY18,U21,Uil,U1B,W19,U20. 06/f'3/81 1'-1 \ ( 08;20 1-0 08;20 Sr1P8078 V 44241552 H 09/12/11 J M ~ .. I Ul.1;AOO;100,AOO"U05,U1e7AOO,AOO~AOO;U04~ I ( v 44241552 M 09112/11 J M U117AOO,AOO,AOO"U05,U18,AOO~AOO;AOO,U04. ; WDP.V.:ri-!G ON LONG SEARCH; TT2~·820 08;80;:44 GETeT; 00300 m I ( 06/23/81 T-I 08;20 1':'0 08=21 Si1P8078 '~ V 44241632 H 08/09/63 A R "4-~ ;;, W19:r !J16, ~J.117 !.-J23, Wif" IrJl 0, bl12; U161 Irl80; Ul!. {.( 1 , 06/23/81 I-I 08:20 1-0 08;21 911P2078 [ i I ( , V 4425269Q H 01/11/62 JJH 1 W28,Wi8,U17,W20,U15,Y88,W13,U17,W20,U17. ! ,.,"" 06/83/81, 7-1 08;20 1-0 08~21 Si1P2078 U it I) 4426.1',1¥. M 09/10/68 IrJ G " W81,U13,U.15,W23"U15,W11;Ul1,U18,W18,U1S. 06/83/81 1-1 08;90 1-0 O£n:21 Si1P9078 I,\1 \.1 4423363Z 11 08/13/53 C P ~ I U20,R14,U08,U20,U15.U15"W17,U11.U14,U15. : 06/23/81 1-1 08;20 1-0 08;81 SMP2078 i V 4425788Y F 10/03/62 M L I { rnII I Ull:rUl0,U09;U14"Ul1,W06,Wl0,Ul0,W1J,U12. V 44257881 F 10/03/62 11 L ._-'J11 ,t~~HhlJ09_" U,1't" tJt~ IrWe,,, til O;t LJ,1 O.! 1:11 3:t Ui 8 "! •• _. ______n If , rnII I 'r ,

I! ,i. ~L\ I kl / I 0~/23/81 1-1 00;22 1-0 OBrr22 SMP2018 V 442098!P M 10/16/58 oR G ( I 06/?~;;/81 T-1 08;20 1-0 08;81 SMP80i'8 Vj8pW19~U13;W17,U09,U23,W17,U21:;Y18,U.17. V 4427431H M 00/00/59 R A O~/23/Bl 1-1 08;22 1-0 08;22 SMP2078 W16:tW09~Wl0:tWl~~U14~W097W097Wi4)rW19,U16. V 4421439M M 03/08/47 J L 4427431H 00/00/59 ( V M R A U:! .!r ;.r U:l :.):; U.1 ci:t U.1. ::; 7 U.1 :3 , t,.} i 5 ~ U.1 ::~ :: U:t ~;' ;: ! J.1 7 ~ U i 7 M I WOO;W09;Wl0,W14~Ui~~W097W09,W147W19pUi6. 06/23/81 1-1 Oa~23 1-0 08=23 SMP2079 06/23/81 1-1 08;21 1-0 08;21 SMP2078 V 4422i14Y M 09/13/61 R F V ~431209R M 07/17/44 M R W24~W13,W1J7W20:;U15:;U247W16,W.16,W2j,U15. II:-•• ~ 111M Ui'u" .,' !!:!(':.I,I'l.=:; .1.1'.:i";! .111'7- ~j1/.j .. I.I::'O-lj1}:::i I U':C'''):tW 0, .. v ... _ ...... ':_ ...... ,.'J.c;:;-_,v... "' . ., ~ ... I_ ,'" "' .... H 06/23/81 1-1 Oa~2J 1-0 08~23 SMP2078 06/23/81 1-1 08~21 1-0 08;21 . SMP2078 ( V 4436548J M 01/04/62 C J V 44Ji3~2P M 01/22/62 J B W18,AOO,AOO~A007U06;W10:;AOO;UOJ~U06~U04. W21:W147U13.W15)rU16~W20,~11,U13pWi67Y14_ ( ( 06/~!:ya1 1-1 OB~23 1-0 08;;:;t:3 E~!·iP:'1078 V 4':31322P i1 01/22/62 J B v 4439645Y H 11/16/60 L R W21~W14)rU13,W15)rU167W20"Wl1,U1J:tW16.U14. U19,W12,W08~U137U15;W81)U16;U17"U157Ui8_ 06/23/81 1-1 08;21 1-0 08:21 SMP2078 1 ( 06/23/81 1-1 08;r23 1-0 08;23 ~mp2078 I V 3830321P F 08/28/42 A S v 4440384R H 07/07/63 V G U19,U14"U13"U16"U1S"U1S"U14"Ul1"U15"W12. W19"WO~~U09"W18.U13,W15"R02"U067U1B,U14_ V 3830321P F 08/28/42 A S ( V 4440324R M 07/07/63 V G ( U19~U14"U13"U16)rU15"U15"U14)rUl1"U1S;UOO. I. W197WO~"U09;W18"U13,W1S7100~U06.U18;U14. 06/23/91 1-1 08;21 1-0 08;21 SMP2078 06/23/81 1-1 08=r::3 1-0 08;23 SMP8078 I) 38'r5752L t1 GDB ( 01127/51 ~ V 4441310N M 01/10/49 R C W20"W17:tW19"W22"W19"U16"W17"W21"W23"W21. W24"U15;U06.U09pU13~U09~U12,U11"Ul0~Ul0. [ V 3845752L M 01/87/51 GDB V 4441310N M 01/10/49 R C . ( W20,W17"W19"W22"W19"U16"Wi7"W21"W23"U21 • I W24~TOO;U06"U09~UiJ,U09~Ui2,Ul1"U10"U10_ 06/23/81 1-1 08;81 1-0 08;21 SMP2078 06/23/81 1-1 08;;23 1-0 08;23 SMP2078 [ V 4225519z K 09/06/58 D C· V 4481J7SK M 00100/62 J S U14;U03~U077U16~U09~W05~U021WOJ,U19"Ul0. W237ROJ:U097W10;U06~U20,U12,U05,U04"U04_ 061 23/Ell 1-1 08;;21 1-0 08::81 st1P2078 V 442137SK M 00/00/62 J S , ' V 4422334M F 05/03/51 C S W2J,TOO;U09,Wl0,U06~U207Ui2,U05~U04"U04. [ W24,Yla,U19~W2e"U18,W28"W16~U14,W22~Wi8. ., ( 06/23/81 1-1 OB=24 1-0 08;24 SMP2078 06/23/81 I-I 08:28 1-0 OB~82 SMP2078 V 442r::249R M 02/28/63 1 C V ~42~812L H 10/26/59 K F W17,100,U06,Ul0~U07,U17,U10,U07~U11;U06. Wil;W13:Wl~;W17,W157U10"U14~U1J;W15;W14. [ ( 06/23/81 1-1 08;24 1-0 08;8.c; S11P;:t078 06/23/81 1-1 08::28 1-0 OB;22 SMP2078 l) .:422515;~ t'i O:~j2ctJ63 JC.;M ( V 4420447P M 01/17/63 S W W19~WOe;U10pUi6~Ul1,Wia~R02"U09,U16"UiO. W13;~14,U13~W83;U20;W1J,Wl1"W.12;W21~U19. [ ( v 4422515N M 03/22/63 JGM 06/23/81 1-1 08;82 1-0 08~22 SMP2078 W19,W08,U10;U16"U11~W18,TOOpU09,U16.UiO_ V 4420567K H 05/24/60 I M 06/23/81 1-1 08;:24 T-O 08.2"', SMP2078 l. Wl~~~OO"U03,W09,W16"W14"AOO;TOO"UOJ"U09. V 4423966J M 02/26/63 R V [ ( V 4420567K M' 05/24/60 I M ( UOS;A007AOO~U.10"U02,AOO,R04;AOO.U08"AOO. Wi2;AOO,U03;W09,W16;Wi41AOO;UOJ"U03~U09. I) '4423966J N 02/26/63 R IJ 06/23/81 1-1 09;22 1-0 OB;22 SMP207B '., ill U05:: AOf),AOO, U.iO" U02" AOO; RO"'r, AOO, jj08~ 100. ( V 4424940N M 10/27/60 A 1 [ ( V ~42396~J M 02/26/63 R IJ W;:·t); !I i .~, U14" I,.J2i ~ !.J.1:;';, It.lr}O, UOH; Ui ~7 ~ l"J 2 r.:' " !Ji8_ U057AOO~AOO,UiO"U02,TOO"R04"AOO~U09,AOO. 06/2~/81 1-1 08~22 1-0 OB~22 ( SMP207B V 4423966J M 02/26/63 R V I) 'tll~"(;':'72R 11 02/2B/62 ... IF:P 'I ~ .' U05,AOO"AOO,Ul0#U02"100,,P.04,A007U08,,100 • [ U12;Rl0FU09~Ui7;WOO,U08~100,U07,Ul1,Ui4_ 06/23/81 1-1 08=24 1-0 08~~4 SMP2078 4420972R 02/28/62 ( V M JAr I.J 4423007P ;1 ·05/03/6-1·-- DTF -- .. U12,R10"U09~U17"Wll,UOB,TOO"U07,Uil;U14_' \ u ( W23"Ri7"W167W18"W20,W237W12"U16~W23~U1B_ [ v 4420972R M 02/28/62 JAP V 4423007P H 05/03/61 DIF U12"Rl0:: U09, V17 J ~.!()O, UOB,AOO; U{)7; U.l 1:r U.tl.-. (' W23~Rj7::W16.W18,W20~W2J;W12~U16,U23.U18. V 4420972R M 02/28/62 JAP 06/23/81 1-1 Oa;2~ 1-0 OB~24 SMP2078 U12;;Rl0;U09"U17,Wli~U08,AOO~U07,U11~U14. V 4427426M H 09107/62 D S [ t.J 442097~'R 1-1 (}~/2a/62 JF:r Ujl~U09,UOa.W19;U12;UOa,U09;U09;W16,UiO. Ui2,Rl0~U09~U17~uoo"uoa~TOO~U07~Ul1~Ui4_ 06/23/81 1-1 08224 1-0 08;24 SMP2078 , v 4420972R M 02/29/62 JAr V 4429452J H 07/12/55 FDr . , i , U12, RiO. U09" U17 ~ Vii:; U081 TOO, UO',']~ U.11:, U:Ur_ W24~W13;U14,W1?~W17,W20"W17~U15~Y16~W1S. V 4420972R M 02/28/62 JAP U12,Rl0::U09:;Ui7;UOO,U08,AOO,U07pU1.1~U14. I: V 4420972R M 02/28/62 JAP " I[ U12,R!OpU09"U17,Ull:;UOS,AOO,U07,Ul1:tU14.

.r .I ------~------_._------I

(> I . ~6/23!al 1-1 08;25 1-0 08~85 SNP2078 V 4430587Y M 07/15/62 A R I Wi8,R15,U13_W16,U17~W17,W13,U1i~U17,Ui7. 06/23/81 1-1 08~25 1-0 08;25 SHP20]8 APPENDIX 9 V 40723212 M 09/12/52 ME U12,U06,U09.U12,UOO,U15,U05,U03,U05,U09. TEST SEARCH 4 HARD COpy I V 4072321Z M 09/12/52 B M U12,U06~U09,U12,UOO,U15,U05,UOJ,UOS,U09. V 40723211 M 09/12/52 MB I U12,U06,U09,U12,U06,U15,U05,U03,U05,U09. V 4072321Z H 09/12/52 B M U12,U06,U09,U12,U10,U15,U05,U03,U05,U09. 06/23/81 1-1 08;25 1-0 08~25 SHP2078 I V 40751589 H 10/21/58 SVD U26,100,UOJ,W15,UOB,U23,R03,U15,U17,Ul0. V 40751589 M 10/21/58 SUD I U26,TOQ,U03,U15,U08,U23,R03,U15,U17,Ul0. 06/23/81 T-I 08:25 1-0 08:25 SMP2078 V 4428570N H 04/14/63 D Q ( U24,Ul0,U15,U16,U19,U18,R17,U17,U1S,U18. I 06/23/Si 1-1 08:25 1-0 08:25 SMP2078 V 4420977H F 09/03/54 ARB U12,U02,ub2,U12,U12,UOJ,U02,U04,U11,U14. I V 4420977H F 09/03/54 ARB U12,U02,U02,U12,U12,U03,U02,100,U11,U14. V 4420977H F 09/03/54 ARB ( U12,U02,U02,U12,U12,U03,TOO,U04,Ul1,U14. F . ( V 4420977H 09/03/54 ARB UJ2,U02~U08~U12$U12;U03,TOO~TOO,Uil;U14. [ 06/23/81 T-I 08:25 1-0 08;25 SMP2078 \ V 4421438M M 03/08/47 J ( U147U1e~U12~U16;U13,W15;U18;U12~U17,U17. 06/23/81 T-I 08;26 1-0 08;26 SMP2078 V 4424823M M 12/09/62 R B E ( WJ7;TOO~TOO,UIJ,Wl~;W177U071U14~U13,U12. V 4424823M H 12/09/62 R B , [ ( W17,TOO,UOj~U13~Wi2,W17,U07,U14;Ui3,U12. V 442!r823N M 12/09/62 R B W17;R02,U01,U131W12;W17;U07,U14;U13~U12. 12/09/62 ( V 442482JH M R B U Wj7,R02,1001U13.W12,W17,U07,U14;U1J~U12. 06/23/81 T-I 08;26 1-0 08;26 SHr207S rn V 4427019J M 06/16/60 C H U Wj5;W11;W12$W197U16;U12~W1e;U14;U15,U15.

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C:=::':::=:~'::'::~:~:-==--:~:':::=:=::'-':==-'~:::'-::::::::'~::::"::~~::':::_'~,=::::'=::',-,,""'''-K,:-,,:",=-=-~,--.~ ... ,-=->,,!=',-.,nJ~''''~'''"'''~_'''''~''~'''_,",,-)Q.~''''''~'"''''''_''''''''''~ ;:X"::::::,--::,,,",::::·::::::::::::::,=::::::::::::::,,:::.'::~=,:,:::,,~::::,:·-r"'"""~"::==::::':::::::'::~.::=:::=:::::::"::::"~_ ,., c:;~ ~~ ="-:1 Il~""", a=~ ~ ~~ Q=:d .::i"'d a~ ~ ~:;: ~fJ ~ ~ ~~ ~::l ~ =:~ ~ ~~ =:::: OF .562 HCORDS bHJ~E HIT AVtRAGE5 0.489 11.191 0.080 5.038 16.797 ,""') Of 1)56 Kc,"ORUS MH.~ HlT Allt:RAufS 0.713 11.459 0.1:58 7.3616 19.677 SEARCH NO = 51 .'l OF U KI:(;OkU;; dEfO~E tUl AlJtRAGES g.OuG 0.000 0.000 0.000 C.oog i •. Of U ~"CORDS AfT ':I~ . I'll r AlJtkAC,Eli .tJOO 0.000 C.OOO 0.000 0.00 l SEARCH NO = 52 Of 6 HtCOKDS d"FOtU; lilT AIJt:RA .. cS 0.000 11.850 0.092 7.491 19 .. 432 Of 145 KLiCO~DS AfTt:R tiJ.T Avt:riAI.IES 1 •.H2 11.89S 0.136 7.491 20.8"'4 "1 SEARCH NO = )3 OF 82 r(c;COKDS dHOr(t: HIT AlieRAGcS 0.000 11.447 0.061 7.485 18.993 Of ld79 ~HORDS AfTck HIT AIJE~A\»ES 1 • .599 11.537 0.140 7.490 20.566 i .1 SEAHCH tfo = )4 :1 O.f 8 RECORDS ~EFvttt. lilT A"':R/.GES 0.000 11.223 0.046 7.486 18.756 I -:> OF 2d9 Kc:COHL>S A f (tr( tHT AlltI

") SEARCH I-lO = 56 OF J KECORDS BE.FJ~e HIT AIJERAIii:S O.OuG O. 000 0.000 0.000 0.000 UF 0 RtiCOt(D~ AFH~ HIT AVERAC.fS 0.000 0.000 0.000 0.000 0.000 .) S£ARCH NO = 57 Of 0 Kt:COHi>S tH;fIlRt; till Allt:KAiiES 0.00(; 0.000 0.000 0.000 0.000 OF 0 RECORDS AfHR HlT AIIEHIIGES 0.000 0.000 0.000 0.000 0.000 )

SEARCH "'0 = 58 ..... OF 14 KECOR~S BHORt: Hll AII CRAuc.S 0.000 10 .. 711 0.068 7.491 18.270 IH 93 S Re:CORII3 HlT Allt:i~A\»fll 1.361 11.223 ._-' AFTt:i< C.135 7.485 20.204 1 f II SEARCH NO = 59 h .) Of 1 tUCORDS tlt.FOr{E HIT AlIc:HAGi;S O.ODO P .000 7.488 Of 141 kceOR!):) Afr.::t. Hl.T AlifRAbc:S 1.2.54 1.296 8·F7• 39 7.482 ~e:na :\ ,~ [ ; ... ) SEARCH ~O = 60 .( Of 1 RHOROS jjfFO~E HlT AvERAGES O.OoG 0.000 0.000 0.000 c.ooo j} Of 61 Rt:CORil:i AFT;;~ HLT AIJEKAGtS 1.41u 11 .. 400 0.116 7.418 20.343 Ii i ._J IiIt .. SEAHCH M ~ 61 \ ot- 7 RECORDS dfFQRE 1111 Av~RAGES 0.00(; 11.953 0.286 7.361 19.599 U.. OF 5 HcCOt(OS AfTEa tiIT A"Et(A<.if~S O.QOO 11.942 u.500 7.443 19.1:!84 " ~ 1~

~ SEAHCH NO = 62 Of 1 Kt.CORO~ d~fOI(i HIT AveRAGeS O.OUO 0.000 0.000 0.000 0.000 . .) OF od J(t:COI(O$ AHcr( Hn A'II t:i

SEARCH NO :r 60S 0 Of d Kt:COHIlS tHFORc rilJ AllckAu~S 1.125 11.339 0.017 4.930 17.411 OF "'~ t(t;ClJfla>S At r t:K tin I\IIt:I1AutS 1.40~ 11.353 O.O7~ 7.347 20.160 .Il. \,) SEARCh NO = b4 j Of loS i{i:(;Ot(IlS dI;FO~c: HIT All t:.RAG ES !J. 7 H 11.296 C.016 5.140 17 .192 (Jf 3.57 Kc:CvkIJS Affl:R 111 I I\IIi:RAbt::> 1.294 11 .328 0.139 7.368 ZG.128 !

It £) ., I~~ SI:ARCH HO = 65 f .Of 5 .~t:COHIl!:i t3c:Fil.u tiIT A":;t(AGcS o.ouo 11.470 0.050 ,7.144 18.665 Or 230 Kc:COfH)S 1\ FT:f( liLT A"li::KAbES 1.416 11.5~d 0.090 7.305 iW.409 !:;)tl 0 ~~_.",,...,..,.,....-.--.-- '_V'~~~"" ~''''''''_._._.~ ., .. __ ___'.""''""'''-'''''''' __ ... _.,.,_ I ~ 0 () .. "- c· -, ':" ~ '" ' ~ , J .. / ' . - .~~ , .. , ... f I . , ,. I .~ .~ ,-_ ..... ~ -

,,1 tl I .~J 1 l II I LATENT FINGERPRINT EXAMINER APPENDIX J..Q. II NEW YORK STATE DEPARTNENT OF CIVIL SERVICE

EXAmNER I S JOB DESCRIPTION I I CLASSIFICATION STANDARD I I NATURE OF WORK Latent Fingerprint Examiners classify, search, and identify fingerprints I lifted from the scenes of crimes and from unidentified bodies. I These positions are found only in the Special Services Unit of the Bureau of Identification and Information Services within the Division of Criminal I I Justice Services. ( :I CLASSIFICATION CRITERIA AND DISTINGUISHING CHARACTERISTICS Positions in this class are characterized by their responsibility for II classifying and coding latent fingerprints and identifying the perpetrator of a crime by comparisons of latent prints to fingerprint cards on file in [ the Division. Typically, the latent fingerprints are incomplete and of poor II quality. Incumbents usually work from photographs of prints taken in situ, which may show distortion from the surface from which the prints are lifted. ~ These conditions complicate the identification of prints. Occasionally, incumbents will classify full prints ,that are difficult to analyze because of dermatological or occupational disfiguration, dust evidence to lift prints, U and identify corpses from lifted prints. TYPICAL ACTIVITIES, TASKS AND ASSIGNr~ENTS ITI [:j Classifies latent fingerprints according to latent print codes or other descriptors. - Codes and references latent fingerprints according to latent print u codes or other descriptors. - Identifies the fingerprint pattern from the photographs of fingerprint o evidence received. - Determines from which finger(s) the print is taken or whether the print is from the palm. ~ - Searches for possible patterns and eliminates prints or patterns that are \L inprpbable - based on knowledge of statistical nature of print ~ patterns for particular fingers. m - Evaluates possible distortion of prints based on the nature of surface ~ I from which the print is lifted. ,1 1u Identifies proper fingers and right or left hand according to their I relationship to themselves and the objects on which the prints are found. ~ I U {)

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\

Enters search argument through computer input devices. make assignments, provide trainin d' ... . Incumbents work with considerableg~ ~n V~rlfY ~osltlve ldentlfications. _ Determines if prints ~an be searched by the quality and amount of detail tentative identifications , and reJ·lnt~penec lng non-matches.ence ln evaluating prints, making obtained from the prints. _ Selects parameters for the search and enters these into the computer for JOB REQUIREMENTS - Good knowledge of the princi 1 f .. . search. cation System and related se~r~~ ~YS~~~s~erlcan Flngerprlnt Classifi- _ Changes parameters, as necessary, if search prints generated provide an insufficient or excessive field of suspects. - ~doOdt~nf?wle~ge1 en 1 lcatlon ofprocess. the policies, procedures ' and guidelines used in the Reviews prints generated to make identifications, using a magnifying eyepiece, to check latent print photographs against print cards. - Good knowledge of latent print codes. _ Compares prints of suspects generated by the initial computer search - Worki~g knowledge of federal, state, and against the latent print. I relatlng to latent print identification. agency procedures and manuals _ Checks new fingerprint cards received against open latent fingerprint . I g - Workingfingerprint knowledge file andof theparamete effe~~ ~~e d f ?rbslear'chln . the computerized cases. and identification. varla e parameters on print field _ When possible identification of a latent print is made, notes points of II comparability and refers to supervisors for verification. .1 I - Working knowledge of procedures for entering computer searches. _ If suspects are identified for particular crimes, checks latent files - ~~~~ ~~~w~edaefof print.pattern~ and probabilities of prints having for unsolved cases in the same geographic area. alne rom partlcular flngers. Classifies or verifies full sets of fingerprints in cases where dermatological ,I - Basic knowledge of print development techniques. or occupational conditions, have distorted prints or resulted in poor prints. - Good knowledge of the effects of surface distortion on latent fingerprints. Compares prints of unidentified corpses against the master print file to make J identifications of the deceased. I - Ability to undertake prolonged and intensive examl'natl'on of fingerprints. Talks to police officers to explain details needed by the section and to obtain information that might be useful in selecting a field of suspects, such I. - ~~~~!;~r~~t~~morize and remember the characteristics of individual as possible suspects, physical descriptions of suspects, and similar crimes committed in the area. - ~bi~~tYlto disce~n the r~lationship between minute points of identification ln e atent prlnt and lnked impression. RELATIONSHIPS WITH OTHERS - Ability to stay mentally alert and concentrate for long periods of time. Latent Fingerprint Examiners orally communicate primarily with their supervisors and other staff in the Special Services Unit. Incumbents also J ~ - Skill in accurately determining match~s and non-matches of prints. have telephone and personal contact with other members of the bureau in processing identifications and fingerprints. - Skill in identifying and classifying partial and dl'storted fingerprints. Incumbents have infrequent face-to-face or telephone contact with police II officers to explain work or obtain additional information.

NATURE OF SUPERVISION m Latent Fingerprint Examiners are not supervisory positions, but may assist in on-the-job training of new Latent Fingerprint Examiners. j positions in this class are supervised by Identification Specialists who ~ I~ ij ~ ",

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Now Yo r k 5 tat"~ D CI.P art m a 'n t ,0 f C i v i IS" r vic e Ann 0 U nee s I Competitive Promotion Examination

Written Test To Be'Held Applications Must Be Postmarked;~ I '. No Later Than I I APRIL 4; 1981 FEBRUARY 23, 1981 NO. 37·212 LATENT FINGERPRINT EXAMIHER G·12 ... . , I THIS EXAMINATION IS OPEN TO ALL QUALIFIED EMPLOYEES OF THE DIVISION qF CRIMINAL JUSTICE SERVICES, EXECUTIVE DEPARTMENT- ,~ .. I At present there are four vacancies in Albany. QUALIFYING EXPERIENCE:

I FOR TAKING THE TEST: On or before the date of the written test, candidates must have had one year of permanent competitive se~Tice as a I senior Identification Clerk G-9. Candidates permanently appointed to a qualifying title on or before April 17, 1980, and who have served continuously in this title since that date, shall be deemed to meet the I Qualifying Experience For Taking The Test. ,1 \ FOR APPOINTMENT FROM I THE ELIGIBLE LIST: two years of the Qualifying Experience For Taking The Test. 1 ' SUBJECT OF EXAMINATION: written test designed to test for the knowledge, skills and/or I abilities in such areas as: o 1. Understanding and interpreting written material

I 2. Effecting identification through the comparison of friction ridges

3. The American Fingerprint Classification System and the identification I operations administered by the NYS Division of Criminal Justice System Points will be added to an eligible score as follows:

I Seniorivd • For Each Year 0.2 I Dutr descriptions are available in your Personnel Office. You May obtain Announcements And Promotion Application Cards, XC-5, [ From Your Agency Personnel Office S-S/T-4-GFJ-bk SEE REVERSE SIDE Issued: 1-16-81 ,~ I NO. 37-212 LATENT FINGERPRINT EXAMINER New York Stahl • ~·-~~""'~~'r-- ,.... ,

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