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Download Biometric Recognition: 7th Chinese Conference, CCBR 2012, by Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Wei-Shi PDF

By Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Wei-Shi Zheng, Zhenan Sun, Yunhong Wang, Xilin Chen, Pong C. Yuen, Jianhuang Lai (eds.)

This e-book constitutes the refereed court cases of the seventh chinese language convention on Biometric popularity, CCBR 2012, held in Guangzhou, China, in December 2012. The forty six revised complete papers have been conscientiously reviewed and chosen from eighty submissions. The papers tackle the issues in face, iris, hand biometrics, speaker, handwriting, gait, delicate biometrics, defense and different comparable issues, and give a contribution new rules to analyze and improvement of trustworthy and sensible suggestions for biometric authentication.

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Read Online or Download Biometric Recognition: 7th Chinese Conference, CCBR 2012, Guangzhou, China, December 4-5, 2012. Proceedings PDF

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Extra resources for Biometric Recognition: 7th Chinese Conference, CCBR 2012, Guangzhou, China, December 4-5, 2012. Proceedings

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1 shows bisquare function and weight function. From Fig. 1, we known that the weights for the bisquare function decline as soon as e departs from 0, and are 0 for |e| > k. Solving the robust estimation problem is equivalent to a weighted least-squares problem [4]. 2 GSRR Algorithm Although SRC in (1) produces a sparse coefficient vector β, it might reconstruct a test sample by training samples from distinct subjects. Recently, the group sparse representation based method has been widely adopted in computer vision [15] [11].

In 2007, Xuan et al. had presented an extensive survey of the passive techniques [32]. In this section, we briefly introduce the representative and the up-to-date techniques to address the problem of illumination variation in face recognition based on single image. Existing methods to solve such a problem fall into two categories: (a) extracting illumination insensitive representation, and (b) restoring a frontal-illuminated face image [4]. (a) Extracting Illumination Insensitive Representation Approaches to extract illumination invariant/insensitive facial representation have almost gone through a development along with the image processing theories.

So, LBP/LTP has the trait of translation invariant and rotation invari ant. Those traits guarantee certain the robustness of LTP to illumination, posture and expression. However, under the illumination with sharp variations, even the local gray gradients are still chanced, thus it is hard to extract stable features relying only on LBP/LTP. For this reason, the LTP doesn’t obtain a good recognition on uncontrolled images, and an illumination preprocessing is necessary. LTV, WeberFace, LOG-DCT, and SQI are all effective to eliminate the illumination, unfortunately, also discard too much low-frequency component.

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