By Jinfeng Yang, Jucheng Yang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng
This publication constitutes the refereed lawsuits of the tenth chinese language convention on Biometric popularity, 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 concentrate on 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.
Read or Download Biometric Recognition: 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings PDF
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Extra resources for Biometric Recognition: 10th Chinese Conference, CCBR 2015, Tianjin, China, November 13-15, 2015, Proceedings
Algorithms for Non-Negative Matrix Factorization. NIPS 13, 556–562 (2001) 4. : Projected Gradients for Non-Negative Matrix Factorization. Neural Computation 19, 2756–2779 (2007) 5. : A Novel Discriminant Non-Negative Matrix Factorization Algorithm with Applications to Facial Image Characterization Problems. IEEE Transactions on Information Forensics and Security 2, 588–595 (2007) 6. : Non-Negative Matrix Factorization in Polynomial Feature Space. IEEE Transactions on Neural Networks 19, 1090–1100 (2007) 7.
Spoof detection schemes. A. ) Handbook of biometrics, pp. 403–423. Springer, US (2008) 5. : Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern. Anal. Mach. Intell. 24, 971–987 (2002) 6. Komulainen, Jukka, Hadid, Abdenour, Pietikäinen, Matti: Face spoofing detection using dynamic texture. ) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 146–157. Springer, Heidelberg (2013) 7. : Face liveness detection based on texture and frequency analyses.
In: Proceedings of the 11th IEEE International Conference on Computer Vision, pp. cn Abstract. The existing Kernel Nonnegative Matrix Factorization (KNMF) cannot ensure the non-negativity of the mapped data in the kernel feature space. This is called the nonnegative in-compatible problem of KNMF. To tackle this problem, this paper presents a new methodology to construct Nonnegative Compatible Kernel (NC-Kernel) for face recognition. We obtain a Nonnegative Nonlinear Mapping (NN-Mapping) by using the techniques of symmetric NMF and nonnegative interpolation strategy.