TY - GEN
T1 - Protecting face biometric data on smartcard with reed-solomon code
AU - Feng, Yi Cheng
AU - Yuen, Pong Chi
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2006/6
Y1 - 2006/6
N2 - This paper addresses the biometric security problem on smartcard and proposes a new method to protect face biometric data against attack. The proposed method is developed based on the Reed-Solomon codes, with two modifications. First, to handle the image variations problem, a bounded distance encoding algorithm is used to reduce the within-class distance. Second, since the dimension of the face feature vector is relatively large, we divide the feature vector into smaller segment. In this way, we found that it will not only affect the performance, but also the security of the encoding algorithm. A theoretical analysis is also given in this paper. Two most popular appearance-based methods, namely, Eigenface and Fisherface, are selected to construct the feature vector and ORL database is used for evaluation. In Eigenface system, the proposed algorithm offers a security level of 120 bits, with an acceptable error rate of around 3%. In Fisherface system, the proposed algorithm offers a security level of 62 bits, with equal error rate around 5%. The results are comparable with performance of Eigenface and Fisherface without protection.
AB - This paper addresses the biometric security problem on smartcard and proposes a new method to protect face biometric data against attack. The proposed method is developed based on the Reed-Solomon codes, with two modifications. First, to handle the image variations problem, a bounded distance encoding algorithm is used to reduce the within-class distance. Second, since the dimension of the face feature vector is relatively large, we divide the feature vector into smaller segment. In this way, we found that it will not only affect the performance, but also the security of the encoding algorithm. A theoretical analysis is also given in this paper. Two most popular appearance-based methods, namely, Eigenface and Fisherface, are selected to construct the feature vector and ORL database is used for evaluation. In Eigenface system, the proposed algorithm offers a security level of 120 bits, with an acceptable error rate of around 3%. In Fisherface system, the proposed algorithm offers a security level of 62 bits, with equal error rate around 5%. The results are comparable with performance of Eigenface and Fisherface without protection.
UR - http://www.scopus.com/inward/record.url?scp=33845514726&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2006.164
DO - 10.1109/CVPRW.2006.164
M3 - Conference proceeding
AN - SCOPUS:33845514726
SN - 0769526462
SN - 9780769526461
T3 - Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop
BT - 2006 Conference on Computer Vision and Pattern Recognition Workshop
PB - IEEE
T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops
Y2 - 17 June 2006 through 22 June 2006
ER -