Biometric Recognition: 8th Chinese Conference, CCBR 2013, by Xun Gong, Jun Luo, Zehua Fu (auth.), Zhenan Sun, Shiguan

By Xun Gong, Jun Luo, Zehua Fu (auth.), Zhenan Sun, Shiguan Shan, Gongping Yang, Jie Zhou, Yunhong Wang, YiLong Yin (eds.)

This publication constitutes the refereed court cases of the eighth chinese language convention on Biometric reputation, CCBR 2013, held in Jinan, China, in November 2013. The fifty seven revised complete papers provided have been conscientiously reviewed and chosen from between a hundred submissions. The papers handle the issues in face, fingerprint, palm print, vein biometrics, iris and ocular biometrics, behavioral biometrics and different similar subject matters, and give a contribution new principles to analyze and improvement of trustworthy and functional strategies for biometric authentication.

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Extra info for Biometric Recognition: 8th Chinese Conference, CCBR 2013, Jinan, China, November 16-17, 2013. Proceedings

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Georghiades et al. [6] showed that the illumination cones of human faces can be approximated well by low dimensional linear subspaces. Therefore, the set of face images in xed pose but under different illumination conditions can be efficiently represented using an illumination cone. Lee et al. [7] showed that the linear subspaces could be directly generated using real nine images captured under a particular set of illumination conditions. These methods can model the illumination variations quite well.

IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 721–732 (1997) 42 L. Wu, P. Zhou, and X. Xu 2. org/FRVT2002/ 3. : FRVT 2006 and ICE 2006 large-scale results. , vol. 7408 (2007) 4. : Ada ptive histogram equalization and its variations. Comput. Vis. Graph. Image Process 39(3), 355–368 (1987) 5. : Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003) 6. : From few to many: illummation cone models for face recognition under variable lighting and pose.

Therefore, these two objective functions are combined as follows, w ∗ = arg max w = arg max w w T SB w w T SW w + γw T Lw w T SB w w T SW w (11) (12) where SW = SW + γL, γ is a regularization parameter in [0, 1]. The solution w ∗ is the eigenvector corresponding to the largest eigenvalue of the generalized eigen-problem: (13) SB w = λSW w . In the RSC, coding residuals at various locations are assumed to be uncorrelated. However, this assumption is often invalid in practice, as analyzed previously.

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