Using biometrics to verify a person's identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. At the same time, improvements in VLSI technology have recently led to the introduction of smart cards with 32‐bit RISC processors. To gain maximum security in verification systems using biometrics, verification as well as storage of the biometric pattern must be done in the smart card. However, because of the limited resources (processing power and memory space) of the smart card, integrating biometrics into it is still an open challenge. In this paper, we propose a fingerprint verification algorithm using a multi‐resolution accumulator array that can be executed in restricted environments such as the smart card. We first evaluate both the number of instructions executed and the memory requirement for each step of a typical fingerprint verification algorithm. We then develop a memory‐efficient algorithm for the most memory‐consuming step (alignment) using a multiresolution accumulator array. Our experimental results show that the proposed algorithm can reduce the required memory space by a factor of 40 and can be executed in real time in resource‐constrained environments without significantly degrading accuracy.
In the modern electronic world, the authentication of a person is an important task in many areas of day-to-day. Using biometrics to authenticate a person's identity has several advantages over the present practices of Personal Identification Numbers (PINs) and passwords. To gain maximum security in the verification system using biometrics, the computation of the verification as well as the store of the biometric pattern has to take place in the security token(e.g., smart card, USB token). However, there is an open issue of integrating biometrics into the security token because of its limited resources(processing power and memory space). In this paper, we describe our implementation of the USB token system having 206MHz StrongARM CPU, 16MBytes Flash memory, and 1MBytes RAM. Also, we describe a fingerprint verification algorithm that can be executed in the restricted environments. To meet the memory space specification and processing power of the security token, in fingerprint verification algorithm, we develop a data structure, called a multi-resolution accumulator array. Based on experimental results, we confirmed that the RAM requirement of the proposed algorithm is about 16 KBytes, and the Equal Error Rate(EER) is 1.7%. Therefore, our fingerprint verification algorithm can be executed in real-time on the developed USB token without degrading accuracy.
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