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

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

This booklet constitutes the refereed complaints of the eighth chinese language convention on Biometric attractiveness, CCBR 2013, held in Jinan, China, in November 2013. The fifty seven revised complete papers offered have been conscientiously reviewed and chosen from between a hundred submissions. The papers deal with the issues in face, fingerprint, palm print, vein biometrics, iris and ocular biometrics, behavioral biometrics and different comparable issues, and give a contribution new rules to investigate and improvement of trustworthy and functional ideas for biometric authentication.

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

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The change of face posture, expression and illumination as well as short occlusion. To solve these problems, early researchers often use the principal adaboost algorithm to detect the face at first, and then track the facial points by the deformable template [1] and active appearance model (AAM) matching method [2]. Although these methods can accurately conduct face contour tracking, but the real-time is not satisfactory. Huang et al. [3] proposed a combination of random forest and linear discriminate analysis (LDA) pose estimation algorithm for tracking the facial feature * Corresponding author.

In RSC, elements of the coding residual are assumed to be independent with some probability density function not necessarily Gaussian or Laplacian. The maximum likelihood estimation principle is utilized to robustly represent the given signal with sparse regression coefficients. To the end, they transformed the optimization problem into an iteratively-reweighted sparse coding problem. The sparse coding model in Eq. t. α 1 ≤ ε, (2) where M is a diagonal matrix of weights assigned to pixels of the query image y .

S= Where σ q and σ t σ qt + C σ qσ t + C are the variance of image q and correlation covariance of image q and σ qt = (1) t respectively. σ qt is the t , as shown in Equation (2). 1 N ∑ (qi − μ q )(t i − μ t ) N − 1 i =1 Where N is the total number of pixels in image q and t . (2) qi and ti are the i th pixel in image q and t respectively. μ q and μt are the average of the corresponding image. where the constant C is included to avoid instability when σ qσ t is very close to zero Therefore, we could say that C is neglectable in theory.

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