Download e-book for kindle: 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 e-book constitutes the refereed lawsuits of the tenth chinese language convention on Biometric attractiveness, CCBR 2015, held in Tianjin, China, in November 2015.
The eighty five revised complete papers awarded have been conscientiously reviewed and chosen from between one hundred twenty submissions. The papers specialize in face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, software and method of biometrics, multi-biometrics and knowledge fusion, different biometric popularity 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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An improved SIFT algorithm is applied in the following matching process. The improved SIFT algorithm employs the learning to rank approach to select the keypoints with higher stability and repeatability instead of manually rule-based method used by the original SIFT algorithm. The proposed face recognition method is evaluated on CASIA 3D face database. And the experimental results show our approach has superior performance than many existing methods for 3D face recognition and handles pose variations quite well.

67–72. IEEE Press, New York (2012) 8. : Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1, 3–10 (2012) 9. : Background subtraction based on a combination of texture, color and intensity. In: International Conference on Signal Processing, pp. 1400–1405. IEEE Press, New York (2008) 10. : Performance of optical flow techniques. Int. J. Comput. Vis. 12, 43–77 (1994) 11. : Computation of component image velocity from local phase information. Int. J. Comput.

Then, the function k(x, y) is denoted by: k(x, y) = ϕ(x), ϕ(y) , where ϕ(x), ϕ(y) is the inner product of ϕ(x) and ϕ(y). It can be directly got that k(x, y) = LT (x)KL(y). (8) In order to show that the function k(x, y) is a Mercer kernel which is defined by (8), the following lemma is used. Lemma 1. [10] If k(x, y) is a symmetric function defined on Rm × Rm , and for any finite data set, it always yields a symmetric and positive semi-definite matrix K = (Kij )n×n , where Kij = k(yi , yj ), i, j = 1, 2, · · · , n, then function k(x, y) is a Mercer kernel function.

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