A Memory-Efficient Fingerprint Verification Algorithm Using a Multi-Resolution Accumulator Array

Etri Journal (Impact Factor: 0.77). 06/2003; 25(3):179-186. DOI: 10.4218/etrij.03.0102.3316


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 multi- resolution 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.

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    • "To avoid faulty matching and improve verification performance, many researchers have investigated the preprocessing of fingerprint images for improving fingerprint verification performance, enhancement of the registered templates' quality, adoption of fingerprint matching methods that can use the extracted templates effectively, and template protection for preventing leaks of registered templates. Many studies on methods for overcoming the problems associated with the fingerprint matching method using direction filters (instead of minutiae), phase information, and fingerprint images have been carried out [10] [11] [12] [13] [14] [15]. "
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    • "For example, an analysis of a student's learning productivity and interest in learning is an effort to determine and then outline the interdependencies between that student's twelve physiological parameters (see Table 1). Different researchers, including Shin et al. (2009), Kong et al. (2006) and Pan et al. (2003), are working in the same field as that of the Physiological computer mouse. This Physiological computer mouse is able to measure the temperature and humidity of a student's hand, skin conductance, touch intensity and heart rate. "
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    • "Thus, it is reasonable to assign the time-consuming steps to the client, rather than to the resource-constrained sensor. This kind of task assignment can be found in the combination of a smart card and card reader [8], [9]. That is, the time-consuming steps are assigned to the more powerful card reader, rather than the resource-constrained smart card. "
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    ABSTRACT: Biometric-based authentication can provide a strong security guarantee of the identity of users. However, the security of biometric data is particularly important as any compromise of the biometric data will be permanent. In this paper, we propose a secure and efficient protocol to transmit fingerprint images from a fingerprint sensor to a client by exploiting the characteristics of the fingerprint images. Because the fingerprint sensor is computationally limited, a standard encryption algorithm may not be applied to the full fingerprint images in real-time to guarantee the integrity and confidentiality of the fingerprint images transmitted. To reduce the computational workload on the resource-constrained sensor, we apply the encryption algorithm to a nonce for integrity and to a specific bitplane of each pixel of the fingerprint image for confidentiality. Experimental results show that the integrity and confidentiality of the fingerprint images can be guaranteed without any leakage of the fingerprint ridge information and can be completed in real-time on embedded processors.
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