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 booklet 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 awarded have been conscientiously reviewed and chosen from between ninety submissions. The papers specialise 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 popularity and processing.

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

This booklet 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 awarded have been conscientiously reviewed and chosen from between ninety submissions. The papers specialise 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 popularity and processing.

Show description

Read or Download Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings PDF

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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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Since the neighbors might be from different classes, we calculate the sum of the contribution to represent the test sample of the neighbors from each class and exploit the sum to classify the test sample. For example, if all the neighbors from the r th ( r ∈ C ) class are xs ... xt , then the sum of the contribution to represent the test sample of the r th class will be Automatic Two Phase Sparse Representation Method g r = bs xs + ... + bt xt 25 (5) We calculate the deviation of g r from y by using Dr =|| y − g r ||2 , r ∈ C (6) We can also convert g r into a two-dimensional matrix with the same size as the original sample image.

Prog. Nat. Sci. 19, 1635–1641 (2009) 19. : Robust Estimation of Foreground in Surveillance Videos by Sparse Error Estimation. In: 19th International Conference on Pattern Recognition, pp. 1–4 (2008) 20. : Sparse Subspace Clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790–2797 (2009) 30 K. Yan, Y. Xu, and J. Zhang 21. : Motion Segmentation via Robust Subspace Separation in the Presence of Outlying, Incomplete, or Corrupted Trajectories. In: IEEE Conference on Computer Vision and Pattern Recognition, pp.

A novel technique for face recognition using range imaging. ISSPA (2003) 47. : Three-dimensional face recognition: an eigensurface approach. In: ICIP (2004) 48. : Three-dimensional face recognition: A fishersurface approach. S. ) ICIAR 2004. LNCS, vol. 3212, pp. 684–691. Springer, Heidelberg (2004) 49. : 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.

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