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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 e-book constitutes the refereed complaints of the eighth chinese language convention on Biometric acceptance, CCBR 2013, held in Jinan, China, in November 2013. The fifty seven revised complete papers offered have been rigorously reviewed and chosen from between a hundred submissions. The papers tackle the issues in face, fingerprint, palm print, vein biometrics, iris and ocular biometrics, behavioral biometrics and different comparable subject matters, and give a contribution new rules to analyze and improvement of trustworthy and functional ideas for biometric authentication.

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

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Illumination Normalization Based on We-ber’s Law With Application to Face Recognition. IEEE Trans. on SP Letters 18(8), 462–465 (2011) 13. : Relative gradients for image lighting correction. In: ICASSP, pp. 549–556 (2010) 14. : Face recognition under varying illumination using gradient faces. IEEE Trans. on IP 18(11), 2599–2606 (2009) 15. : Lightness and Retinex Theory. J. Opt. Soc. Am. 61(1), 1–11 (1971) 16. : Properties and performance of a center/surround Retinex. IEEE Trans. Image Process.

Springer International Publishing Switzerland 2013 44 T. Zhao et al. points, and then used the online appearance model (OAM) [4] and incremental principal component analysis (PCA) [5] to update the AAM texture model. This method efficiently solves the change of illumination, expression and poses during tracking. Cui et al. [6] proposed to use Lucas-Kanade optical flow algorithm [7] to align the human face based on the traditional AAM. Although this method is realtime, but cannot effectively solve the posture change or temporary occlusion, and is not robust for real applications.

This leads to solving an optimization problem of the form min y − W s s 2 2 + λ s 0 , s ≥ 0, λ ≥ 0. (6) Here notation · 0 signifies the number of non-zero elements in a vector and λ is a regularization parameter. However, above l0 -norm least squares problem (6) is very hard to tackle since it is an NP-hard problem[4]-[6]. Fortunately, if the solution s is sufficiently sparse, then the regularization term s 0 can be equivalently replaced by s 1 such that the problem (6) becomes convex. So, we can solve the following l1 -regularized least squares problem to obtain an non-negative sparse solution: min y − W s s 2 2 + λ s 1 , s ≥ 0, λ ≥ 0.

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