Download Biometric Recognition: 9th Chinese Conference, CCBR 2014, by Zhenan Sun, Shiguang Shan, Haifeng Sang, Jie Zhou, Yunhong PDF

By Zhenan Sun, Shiguang Shan, Haifeng Sang, Jie Zhou, Yunhong Wang, Weiqi Yuan

This e-book constitutes the refereed lawsuits of the ninth chinese language convention on Biometric popularity, CCBR 2014, held in Shenyang, China, in November 2014. The 60 revised complete papers provided have been conscientiously reviewed and chosen from between ninety 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 knowledge fusion, different biometric attractiveness and processing.

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Additional info for Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings

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2 Random Forest Tra ain and Classification We randomly selected 1256 scans of CASIA 3D face database. From each scan, we manually selected 20 pointss correspond to skin parts such as face, ear and neck. T Then we selected 20 points correesponding to non-skin parts such as hair and clothes. T The color information of each point p was recorded as (R, G, B). Then the RGB color sppace was transformed into HSV space. 2, where skin points are marked with ‘o’ and non-skin points are marked with ‘ ’. As we can see from Figure 1 and Figure 2, these skin color pooints gathered, while non-skin color points distributed around them.

Pattern Recognition Letters 28(10), 1252–1262 (2007) 4. : A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006) 5. : 2D and 3D face recognition: A survey. Pattern Recognition Letters 28(14), 1885–1906 (2007) 6. : 3D face recognition with sparse spherical representations. Pattern Recognition 43(3), 824–834 (2010) 7. : Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition.

E. , L} . If no neighbor is from the p th class, then the number p must be not an element of C . Consequently, the TPSR will not ultimately classify the test sample into the p th class. 2 The Second Phase of the TPSR The second phase of the TPSR seeks to represent the test sample as a linear combination of the determined Μ nearest neighbors and uses the representation result to classify the test sample. This phase assumes that the following equation is approximately satisfied: y = b 1 x1 + ...

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