Download Biometric Recognition: 6th Chinese Conference, CCBR 2011, by Zhaoxiang Zhang, Chao Wang, Yunhong Wang (auth.), Zhenan PDF

By Zhaoxiang Zhang, Chao Wang, Yunhong Wang (auth.), Zhenan Sun, Jianhuang Lai, Xilin Chen, Tieniu Tan (eds.)

This e-book constitutes the refereed lawsuits of the sixth chinese language convention on Biometric attractiveness, CCBR 2011, held in Beijing, China in December 2011. The 35 revised complete papers have been rigorously reviewed and chosen from seventy one submissions. The papers are equipped in topical sections on difficulties in face; iris; hand biometrics; speaker; handwriting; gait; behavioral and delicate biometrics; and security.

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Additional resources for Biometric Recognition: 6th Chinese Conference, CCBR 2011, Beijing, China, December 3-4, 2011. Proceedings

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The whole feature vector W is achieved by using formula (6). The difference of this paper, we won’t use PCA to reduce the feature vector dimension of each pixel so as to preserve entire local structure information for image decomposition. In other words, we not only pay more attention to the characteristic features of each pixel, but also concern the weak structure information in this way. Therefore, one can get the completed local structure features of an image. 2 Image Decomposition and Feature Representation After obtaining the local structure information, we can decompose an image into several sub-images according to the corresponding local feature vectors of each pixel.

5. Deduplication is a new function of our system, and obviously it is of great importance in household management. The new version of THFR system equipped with deduplication function has been put into application of household management in some provinces of China already, and it played a significant role in management of the ID card system. Fig. 5. A typical result of deduplication application – a pair of duplicated entries in the database 5 Conclusion In this paper, we studied the stability of facial feature points and proposed two algorithms to fuse the feature points in THFR system.

C) Finally, a normalized 128-element vector (4×4×8) is formed in the block. From Procedure 1, we can see that the time consumption of SLFD can be reduced greatly compared to SIFT. More important, the feature descriptors obtained using SLFD are more discriminative than SIFT descriptors for face recognition. First, by keeping edge response keypoints, the important edge information which is critical for face recognition can be used in SLFD. Second, without orientation alignment, the coordinates of the descriptors and the gradient orientations do not need to rotate relatively to the keypoint dominant orientation, so all the SLFD are computed under the same coordinate, and the false matching caused by the orientation assignment can be avoided since face images are extracted and aligned in advance for recognition.

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