VOICE ENABLED ATM MACHINE WITH IRIS 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- Automated Teller Machine, 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, fingerprint, 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 biometrics 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.