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

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

This publication constitutes the refereed court cases of the ninth chinese language convention on Biometric attractiveness, CCBR 2014, held in Shenyang, China, in November 2014. The 60 revised complete papers provided have been rigorously reviewed and chosen from between ninety submissions. The papers concentrate on face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, program and procedure of biometrics, multi-biometrics and data fusion, different biometric acceptance and processing.

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

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3D face recognition using local shape map. In: ICIP (2004) 50. : Matching tensors for pose invariant automatic 3d face recognition. In: CVPRW (2005) 51. : Face authentication or recognition by profile extraction from range images. In: Workshop on Interpretation of 3d Scenes (1989) 52. : 3D facial image analysis for human identification. In: ICPR (1992) 53. : Face authentication based on multiple profiles extracted from range data. In: AVBPA (2003) 54. : Exploring face recognition by combining 3d profiles and contours.

TIFS 2(3), 537–547 (2007) 79. : Three-dimensional face recognition using shapes of facial curves. TPAMI 28(11), 1858–1863 (2006) 80. : An intrinsic framework for analysis of facial surfaces. IJCV 82(1), 80–95 (2009) 81. : Boosting 3-d geometric features for efficient face recognition and gender classification. TIFS 7(6), 1766–1779 (2012) 82. : Pose and expressioninvariant 3d face recognition using elastic radial curves. In: BMVC (2010) 83. : Description and retrieval of 3d face models using iso-geodesic stripes.

3. (3) and solve this equation. 4. (6) to compute deviation Dr which is generated from the r th class, r ∈C . 5. To classify the test sample into the class that has minimum deviation. In other words, if Dq = min Dr ( q, r ∈ C ) , the test sample will be classified into the q th class. e. vector Rt is composed of all entries of Dr in ascending order. Let vt = log( Rt (2) / Rt (1)) . Rt (1) and Rt (2) are the first two smallest entries of Rt . 26 K. Yan, Y. Xu, and J. Zhang 6. After steps 1-5 have been implemented for all test samples, we identify 10 percent of the test samples with the smallest vt and take the mean of vt s of these test samples as u L .

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