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

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

This publication 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 provided 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, software and procedure of biometrics, multi-biometrics and data fusion, different biometric reputation and processing.

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

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International Journal of Pattern Recognition and Artificial Intelligence 26(01) (2012) 12. : Overview of the face recognition grand challenge. In: IEEE Workshop on Face Recognition Grand Challenge Experiments, pp. 947–954 (2005) 13. : Distinctive image features from scale-invariant keypoints. International Journal of Computer and Vision 60(4), 91–110 (2004) 14. : Rank-SIFT: Learning to rank repeatable local interest points. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp.

The general framework is illustrated in Fig. 1. Point cloud Preprocessing Sphere depth LBP image representation Local feature extraction and matching Fig. 1. General framework of proposed method The paper can be summarized as follows: Section 2 describes the point cloud preprocessing and the sphere depth image generation. In section 3, we introduce the local features matching on sphere LBP depth image utilizing learning to rank strategy. The experimental results are provided in section 4. Section 5 concludes the paper.

In KPCA-based methods, the parameters of RBF kernel function is set as t = 5 × 103 . The whole database is randomly divided into training set and test set. The accuracy is computed using nearest neighbor classifier. The experiments are repeated 10 times and the average accuracies are recorded. 1 Experiments on ORL Database In the ORL database, there are 400 gray images with 40 persons. Each person has 10 images with different poses and expressions. Images from one person are shown in Figure 1. The size of each image is 112×92.

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