VOICE ENABLED ATM MACHINE WITH RECOGNITION FOR AUTHENTICATION

1R.D.SALAGAR, 2AKSHATA PATIL

1 Associate Professor Department of Computer Science and Engineering B.L.D.E.A’s Dr. P. G. H Engineering college Bijapur, Karnataka, India 2Mtech Computer Science and Engineering B.L.D.E.A’s Dr. P. G. H Engineering college, Bijapur, Karnataka, India

Abstract- Automated teller machines (ATMs) are a classic example of ubiquitous computing as they pervade our everyday life. Security is of paramount importance during ATM transactions. People choose passwords which are easy to remember, and, typically, easily predicted, or they change all PINs to be the same. Another concern is the accessibility of ATM machines to differently abled people. These concerns can be overcome by using iris recognition for authentication and voice enabled transactions in ATM machines. The iris patterns of the two eyes of an individual or those of identical twins are completely independent and uncorrelated. Iris recognition involves pre-processing, feature extraction and matching. Matching is done by comparing the user iris with the iris database images which were acquired at the time of opening an account in the bank. Once the authenticity of the user iris is verified, the user is allowed to carry out further transactions using voice based commands by speaking into a microphone. This model not only ensures security but also easy accessibility to certain sections of the population like people with visual impairments.

Key words- , Iris recognition, Voice transaction.

I. INTRODUCTION commercial iris recognition systems for various security applications. It is the most promising among "ATM" stands for Automated Teller Machine. ATM various biometric techniques (face, , palm machine was invented by John Shepphardbaren on vein, signature, palm print, iris, etc.) because of its June 1967 at Barclays bank in Enfield, United unique, stable, and noninvasive characteristics. The Kingdom. In India, Hong Kong and Shanghai iris patterns of the two eyes of an individual or those banking corporation (HSBC) installed first ATM in of identical twins are completely independent and 1987[1] . uncorrelated. Iris recognition system can be used to either prevent unauthorized access or identity Traditionally, access to secure areas or sensitive individuals using a facility. When installed, this information has been controlled by possession of a requires users to register their system. A distinct iris particular artifact (such as a card or key) and/or code is generated for every iris image enrolled and is knowledge of a specific piece of information such as saved within the system. Once registered, a user can a Personal Identification Number (PIN) or a present his iris to the system and get identified. password. Today, many people have PINs and Enrollment takes less than 2 minutes. Authentication passwords for a multitude of devices, from the car takes less than 2 seconds [3]. radio and mobile phone, to the computer, web-based services and their bank information. Herein lies a Voice-activated automatic teller machines were major difficulty involving the trade-off between designed to help people with visual impairments, usability, memorability and security [1],[2]. including some elderly people, make financial transactions. Not every blind person can read Braille, Term refers to any and all of a variety of and so ATM’s equipped with Braille keypads don’t identification techniques, which are based on some always suffice. physical or behavioral characteristics of the individual, contrasted with those of the wider In addition, Braille keypads may allow blind people population. Physiological biometric techniques to enter the information they need to, but they don’t include those based on the verification of fingerprint, provide a means of delivering directions to visually- hand and/or finger geometry, eye (retina or iris), face, impaired customers. wrist (vein), and so forth. Behavioral techniques So unless a blind person were to walk into a bank include those based on voice, signature, typing already knowing exactly how to use the ATM, it behavior, and pointing. might not be possible for him or her to make transactions without assistance from a bank Iris recognition is a rapidly expanding method of employee. Indeed, in the past, some visually-impaired biometric authentication that uses pattern-recognition people tended to avoid ATM’s altogether. However, a techniques on images of irises to uniquely identify an voice-activated ATM solves most, if not all, of those individual have been extensively deployed in problems [4].

Proceedings of 3rd IRF International Conference, 10th May-2014, Goa, India, ISBN: 978-93-84209-15-5 70 Voice Enabled ATM Machine with Iris Recognition for Authentication II. LITERATURE SURVEY nolonger optional for financial institutions in the United States; it’s mandatory. That’s because, As a focus for human–computer interaction (HCI) between 2004 and 2010, the U.S. Department of research, the ATM and its usability characteristics Justice handed down a series of rulings on the issue have attracted a limited amount of attention (Johnson of voice-activated ATM’s. The result of these & Westwater, 1996) when compared with the large decisions was that, as a new stipulation of the and varied body of research addressing the domain of Americans with Disabilities Act, or ADA, all banks, the desktop personal computer (PC) and occupational credit unions and other financial institutions were settings such as control rooms. To some extent, this required to install at least one voice-activated ATM in appears to be due to the narrow task scope, wide user every location where they maintained ATM’s. population, and relative operational success of many financial self-service interfaces[5]. III. METHODOLOGY

There has been a steady and major stream of ATM- The intelligent Biometric-based access for ATMs focused research in the United States, mainly consists of the following components: centered on the issues concerning older users, their technology use, and training approaches (Jamieson, 3.1 Iris Recognition module Cabrera, Mead, & Rousseau, 1995; Jamieson & Iris Recognition systems can be explained as follows: Rogers, 1998; Mead & Fisk, 1998; Rogers, Cabrera,  Image Acquisition Walker, Gilbert, & Fisk, 1996; Rogers & Fisk, 1997; Rogers, Fisk, Mead,Walker, & Cabrera, 1996;  Iris Preprocessing which includes Rogers, Gilbert, & Cabrera, 1994, 1997; Smither, localization and segmentation Braun, & Smither, 1991).  Iris Normalization

Daugman made use of multiscale to demodulate  Feature Extraction and texture phase structure information of the iris[6].  Matching Flom and Safir first proposed the concept of automated iris recognition in 1987[7]. Iris matching 3.2 Voice Transactions module was performed by computing Euclidean distance A microphone commonly used in computer system is between the input and the template feature vectors. used as voice sensor to record the ATM user voice. Kumar to measure the consistency of iris images from The recorded voice is then sent to the system which the same eye Filtering an iris image with a family of will identify the command given by the user based on filters resulted in 1024 complex-valued phasors his/her voice. which denote the phase structure of the iris at different scales. Each phasor was then quantized to This system has the following modules: one of the four quadrants in the complex plane. The  Training phase resulting 2048 component iriscode was used to  Testing (operational) Phase describe an iris. The difference between a pair of iriscodes was measured by their Hamming distance. 1.1 IRIS RECOGNITION Similar to the matching scheme of Daugman, they Iris Recognition systems can be explained as follows: sampled binary emergent frequency functions to form i. Image Acquisition ii. Iris Preprocessing which a feature vector and used Hamming distance for includes and segmentation iii.Iris Normalization matching. iv.Feature extraction and v. Matching

Voice-activated ATM’s are not new. Banks large and small began rolling out this technology early in the first decade of the twenty-first century. For example, all new ATM’s purchased by Australian banks since 2003 have been voice-activated; banks in that nation began installing voice-activated ATM’s as part of a pilot program in 2002. Also in 2002, Banknorth, a small American chain of banks with headquarters in Fig 1: Block diagram of iris recognition Portland, Maine, began to install voice-activated ATM’s in 400 of its banks, a program that was 3.1.1 Localization of Iris with Canny Edge Detection completed in cooperation with the National Canny Edge Detection technique used for Federation of the Blind. In the end, Banknorth – now segmentation and it is implemented using image TDB Banknorth – spent five years and almost five management tool in LABVIEW and vision module. million dollars to get these machines operational [8]. Here, after getting the input image, the next step is to TDB Banknorth and others may have voluntarily set localize the circular edge in the region of interest. up voice-activated ATM’s, but today doing so is Canny edge detection operator uses a multi-stage

Proceedings of 3rd IRF International Conference, 10th May-2014, Goa, India, ISBN: 978-93-84209-15-5 71 Voice Enabled ATM Machine with Iris Recognition for Authentication algorithm to detect a wide range of edges in images. It is an optimal edge detector with good detection, Concept of LBP: The LBP feature vector, in its good localization and minimal response. simplest form, is created in the following manner: 1. Divide the examined window to cells (e.g. In localization we used this detection, in which the 16x16 pixels for each cell). inner and outer circles of the iris is approximated, in 2. For each pixel in a cell, compare the pixel which inner circle corresponds to iris/pupil boundary to each of its 8 neighbors (on its left-top, and outer circle corresponds to iris/sclera boundary. left-middle, left-bottom, right-top, etc.). But the two circles are usually not concentric. Also, Follow the pixels along a circle, i.e. comparing with other parts of the eye, the pupil is clockwise or counter-clockwise. much darker. The inner boundary is detected between 3. Where the center pixel's value is greater the pupil and the iris. At the same time, the outer than the neighbor, write"1". Otherwise, write boundary of the iris is more difficult to detect because "0". This gives an 8-digit binary number of the low contrast between the two sides of the (which is usually converted to decimal for boundary. So, we detect the outer boundary by convenience). maximizing changes of the perimeter normalized 4. Compute the histogram, over the cell, of along the circle. Iris segmentation is an essential the frequency of each "number" occurring process which localizes the correct iris region in an (i.e., each combination of which pixels are eye image. Circular edge detection function is used smaller and which are greater than the for detecting iris as the boundary is circular and center). darker than the surrounding. 5. Optionally normalize the histogram. 6. Concatenate normalized histograms of all 3.1.2 Normalization of Iris Using Gabor Filter cells. This gives the feature vector for the In normalization, the obtained iris region is window. transformed in order to have fixed dimensions for the purpose of comparison. Gabor filter is used for the purpose of normalization. It is a linear filter used for edge detection. Here it is used to perform good detection of iris region. The size of the pupil may change due to the variation of the illumination and the associated elastic deformations in the iris texture may interface with the result of pattern matching. And so, for the purpose of accurate texture analysis, it is necessary to compensate this deformation. Since we have detected both inner and outer boundaries of the iris, it is easy to map the iris ring to a rectangular block of texture of a fixed size. Here a convolution filter also employed for the purpose of enhancement.

The original image has low contrast and may have Fig 2: Example of LBP non- uniform illumination caused by the position of the light source. These may impair the result of the 3.1.4 Matching: texture analysis. We enhance the iris image in order Here, matching of two iris code is performed using to reduce the effect of non uniform illumination. The the Hamming distance. The Hamming distance gives one-dimensional Gabor filter is defined as the a measure of how many bits are the same between multiplication of a cosine/sine (even/odd) wave with two bit patterns. Using the Hamming distance of two a Gaussian window as follows, bit patterns, a decision can be made as to whether the two patterns were generated from different irises or (x) = eσ cos(2πω x) √πσ from the same one. In comparing the bit patterns X (1) and Y, the Hamming distance, HD, is defined as the 1 sum of disagreeing bits (sum of the exclusive-OR (x) = e sin(2πω x) (2) √2πσ between X and Y) over N, the total number of bits in 3.1.4 Feature Extraction with Local Binary Pattern: the bit pattern. N Local Binary patterns (LBP) are a type of feature 1 used for classification in computer vision. LBP was HD = X(XOR)Y N first described in 1994.It has since been found to be a powerful feature for texture classification; it has further been determined that when LBP is combined The Hamming distance is the matching metric with the Histogram of oriented gradients (HOG) employed by Daugman and calculation of the classifier, it improves the detection performance Hamming distance is taken only with bits that are considerably on some datasets. generated from the actual iris region.

Proceedings of 3rd IRF International Conference, 10th May-2014, Goa, India, ISBN: 978-93-84209-15-5 72 Voice Enabled ATM Machine with Iris Recognition for Authentication 3.2 VOICE BASED COMMAND MODULE 3.2.1 Training phase Second, and the main focus of this project, was the Training enrolment as shown, the persons are novel interaction embraced by the ATM concept. To registered and their voices are recorded. The recorded demonstrate an alternative, humanlike interaction, voices are then extracted. Features extracted from the speech synthesis and speech recognition were used recorded voices are used to d envelop models of for the interaction rather than a standard keypad or persons. touch screen. In this scenario user speaks into the microphone. microphone capture sound waves and 3.2.2 Testing (operational) Phase generates electrical impulses and sound card converts Testing or operational phase in this phase a person voice signal into digital signal who wants to access the ATM is required to enter the claimed identity and his/her voice. The entered voice is processed and compared with the claimed person model to verify his/her claim. At this point system decides whether the feature extracted from the given voice matches with the model of claimed person. Threshold is set in order to give a definite answer of access acceptance or rejection. When degree of similarity between a given voice and model is greater than threshold the system will accept the access, otherwise the system will reject the person to access Fig the ATM. 3: Voice based transaction 3.2.3 System Architecture The system has the following phases: The architecture describes the details of the  Training phase Automated Biometric Voice-Based Access Control in  Testing (operational) Phase Automatic Teller Machine operations performed are

released by the Institute of Automation in Chinese 3.2.4 Feature Extraction and MFCC Academy of Sciences. The CASIA V1.0 iris database  MFCC (Mel Frequency Cepstral is a classic iris set which contains 756 iris images Coefficient) is used in feature extraction from 108 subjects, in which iris textures are clear and 1. Frame the signal into short frames. there are seldom noises. 2. For each frame calculate the of the power spectrum. 3. Apply the mel filter bank to the power spectra, sum the energy in each filter. 4. Take the logarithm of all filter bank energies. 5. Take the DCT of the log filter bank energies.

IV. RESULTS AND DISCUSSION Fig-5 Iris Recognition Process: (a) The original eye image taken from CASIA iris database (b) In this work, CASIA V1.0 iris database is used to Region of interest extracted image (c) Filtered iris image and evaluate the proposed methods. This iris database is (d) Edge detected portion of the iris textures

Proceedings of 3rd IRF International Conference, 10th May-2014, Goa, India, ISBN: 978-93-84209-15-5 73 Voice Enabled ATM Machine with Iris Recognition for Authentication We will test the performance of our iris indexing Table I: False rejection for iris and voice method on it. Here, proposed system simulation After the iris is identified the user’s name is displayed results are discussed. The simulation is performed on the screen. The user has to confirm by saying using MAT LAB software. “yes” to carry on further transactions or “no” to abort transactions. Next the user has to choose the account The above figures are the results of iris recognition type by saying “savings” or “current”. After that the process. In which, fig 5(a) is the original eye image user will enter the PIN number followed by the taken from CASIA iris database. The eye image is amount to be withdrawn and a receipt can be processed to segment the region of interest portion as obtained. shown if fig 5(b). After this, the extracted image is filtered to get the patterns of clear iris textures as CONCLUSION shown in fig 5(c). Fig 5(d) shows the canny edge detected portion of the filtered iris textures. The In this paper, the iris recognition is discussed by following figure shows the simulation results of using the MAT LAB software. Here, initially input database creation and matching. eye images are uploaded from database and region of interest segmentation and localization of iris using canny edge detection is performed. Use of Canny edge detection provides good localization and detection which in turn provides time consumption. Also normalization of iris is performed using the Gabor filter and feature vectors are extracted using Local Binary Pattern (LBP) and classification is performed using Learning Vector Quantization (LVQ). Here, matching is performed using hamming distance. Also we create a Lab VIEW database for Fig 6: creation of database storing the information of the users. All the images used in this paper were collected from the Chinese Academy of Sciences Institute of Automation (CASIA) iris database VI.0 with 108 subjects in it.

FUTURE SCOPE

Fig 7: steps in iris recognition In future, we planned to enhance this iris recognition system for real-time images using MAT LAB. The real-time capturing is possible by use of digital cameras compatible with USB. In which the resolution of camera must be at least 5 mega pixel and it must be able to process 18 frames/sec for clear capture of images.

Our future research will also address the issues of Fig 8: authorized person privacy through kiosk design, through the use of highly directional speakers to create private audio zones that do not require physical barriers; the quality of speech input through utilizing highly directional microphones. We also plan to introduce voice commands in various languages so that even people without knowledge of English can use ATM facility.

ACKNOWLEDGEMENT

We thankfully acknowledge the Chinese Academy of Sciences Institute of Automation for providing us the CASIA I and CASIA II Iris Image Database.

REFERENCES

[1] Johnson GI, Coventry L. “You Talking to Me?” Exploring Voice in Self-Service User Interfaces. International Journal of Human–Computer Interaction 2001, 13(2), 161–186.

Proceedings of 3rd IRF International Conference, 10th May-2014, Goa, India, ISBN: 978-93-84209-15-5 74 Voice Enabled ATM Machine with Iris Recognition for Authentication [2] Michael Negin Thomas A. Chmielewski Jr, Marcos [6] Yekini N.A., Itegboje A.O., Oyeyinka I.K., Akinwole A.K. Salganicoff, Theodore A Camus, Ulf M. Cahn von Seelen, Automated Biometric Voice-Based Access Control in Péter L. Venetianer, Guanghua G.Zhang. An Iris Biometric Automatic Teller Machine (ATM). IJACSA 2012,3(6), 71- System for Public and Personal Use. IEEE 2000. 77. [3] Sheeba Jeya Sophia S, Veluchamy S. Security System [7] Sunil Lohiya. Biometric identification and verification Based on Iris Recognition. Res. J. Engineering Sci. techniques-A future of ATM Banking System. Indian 2013,Vol. 2(3), 16-21. Streams Research Journal 2012, 2(7), 1-6. [4] Tpoar. Voice Activated ATMs and New ADA [8] Mini Agarwal, Lavesh Agarwal. Accessing the bank Requirements. http://www.atmdepot.com 01-11-2013. account without card and password in ATM using biometric technology. Researcher, 2012;4(3), 33-37. [5] Lynne Coventry, Antonella De Angeli, Graham Johnson. Usability and Biometric Verification at the ATM Interface. CHI 2003, 5(1),153-160.

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Proceedings of 3rd IRF International Conference, 10th May-2014, Goa, India, ISBN: 978-93-84209-15-5 75