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Latent Fingerprint Identification System Report National Criminal Justice Reference Service nCJrs This microfiche was produced from documents received for inclusion in the NCJRS data base. Since NCJRS cannot exercise control over the physical condition of the documents submitted, the individual frame quality will vary. The resolution chart on this frame may be used to evaluate the document quality. 2 5 1.0 :; IIIP'S 11111 . w ~1113.2 hi, 2.2 ~ pc I:: Ii£ I:i w ...'" u 1.1 "'''" I 111111.25 111111.4 111111.6 I. MICROCOPY RESOLUTION TEST CHART NATIONAL BUREAU Of STANOARDS-1963-A ,. " Microfilming procedure.s used to create this .fiche comply with ....'.'1 ;. the standards set forth in 41CFR 101-11.504. 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 .. ,- ~~~b-M- ~ 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 " .. , rn~H , ¢ •• 'k.. ..... 'O',.""'.~.- , ...... , __ •.t~ • ..;.o""" .. "'-•.....,. 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. " \ 2 ....... ' .0 /. " r i ,. ./ , \ / I / " 78 60 L&J ..J Il.. i ::E 50 I, !l· C1i I I! r! LL ", 0 / 1t " ,1 .... / " FIGURE 1 '\ , ' :z :1 L&J / 'j 4et , \ U 1 FINGER SEARCHES I a:: , f .\' L&J I' Il.. 2 FINGER SEARCHES i i·fI , \ II 3 FINGER SEARCHES , \ 30+ I! ! I I ~ 1 \ iI\' 28 U tl,I til I I \~ " e~~1.i0~--~-·~~Bin~--~-+---,~~~--~t--~4.~Br-~~ LIST SIZE ..... -, /. I .I . ' . ___ ,. ___ '_---------------------------- I 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 I 3 D ! , 4 , . !1-1 I n . !, / ---------------------- .. I 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.
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