Biometric Recognition: 10th Chinese Conference, CCBR 2015, by Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan,

By Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng

This booklet constitutes the refereed complaints of the tenth chinese language convention on Biometric attractiveness, CCBR 2015, held in Tianjin, China, in November 2015.
The eighty five revised complete papers offered have been rigorously reviewed and chosen from between a hundred and twenty submissions. The papers specialize in face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, program and approach of biometrics, multi-biometrics and data fusion, different biometric popularity and processing.

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Additional resources for Biometric Recognition: 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings

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It is a significant measure of the keypoint robustness and stability. We can select keypoints according to their repeatability. We use the learning to rank algorithm [14] to train a ranking model to rank keypoints according to their stability. The features utilized in the ranking model are associated with the steps in keypoints extraction and removal, including the first/second derivatives of the depth image, the eigenvalues (λ1, λ2), determinant Det (H), and the eigenvalue ratio Trac (H)2/Det(H) of the Hessian matrix H.

FKn . , Norm(fKn )], we carried out our experiment on such manner instead of concatenating all discriminant sub-feature together for the sake of saving memory in some databases. Such process could also be carried out after feature projection using classifiers as a mean of similarity fusion. 2nd Convolution Layer st 1 Convolution Layer Pooling Layer Input Layer Filtered Image Patch-mean Removal Filtered Image Patch-mean Removal Patch-mean Removal Filtered Image Nonlinear Projected Image Filtered Image Nonlinear Projected Image Image Filtered Image Patch-mean Patc Removal Filtered Image Patch-mean mean Removal Patch-mean mean Removal Filtered Image Nonlinear Projected Image Filtered Image Nonlinear Projected Image Fig.

The keypoints marked with ellipse are eliminated by the removal strategies. But these keypoints can represent distinctive structures, and are in the area of high repeatability with the pose changes. Therefore, the keypoint removal scheme in the SIFT framework will eliminate some useful features when applied to face images. 2 H. Wang et al. Ranking Keypoints in Keypoints Selection To overcome the problem above, we adopt a supervised approach to select the keypoints rather than using thresholds. Keypoint repeatability R(xi) is defined as the times that the same keypoints appear in a sequence of same person’s face images.

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