Get Biometric Recognition: 9th Chinese Conference, CCBR 2014, PDF

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

ISBN-10: 3319124838

ISBN-13: 9783319124834

ISBN-10: 3319124846

ISBN-13: 9783319124841

This e-book constitutes the refereed lawsuits of the ninth chinese language convention on Biometric attractiveness, CCBR 2014, held in Shenyang, China, in November 2014. The 60 revised complete papers offered 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 knowledge fusion, different biometric acceptance and processing.

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

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16 D. Huang et al. 16. : 3-D face recognition using elbpbased facial description and local feature hybrid matching. TIFS 7(5), 1551–1565 (2012) 17. : 3D face recognition under expressions, occlusions, and pose variations. TPAMI 35(9), 2270–2283 (2013) 18. : An efficient multimodal 2d-3d hybrid approach to automatic face recognition. TPAMI 29(11), 1927–1943 (2007) 19. : Keypoint detection and local feature matching for textured 3d face recognition. IJCV 79(1), 1–12 (2008) 20. : Textured 3d face recognition using biological vision-based facial representation and optimized weighted sum fusion.

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. 1–8 (2008) 22. : Sparse representation or collaborative representation: Which helps face recognition? In: IEEE International Conference on Computer Vision, pp.

3 (c) showed the prrocessed final point cloud of a face. It is clear that the proposed method can recov ver eyebrows, eyes and mouth, etc. and some noise cann be removed too. 4 Experiments To test the skin color deteection method, we conducted a series of experiments on CASIA 3D face database. 2, we manually seleccted a non-skin color points respectively. Then we perform med 1256*20 skin color points and validation on the selected points. In random forests, there was no need for croossvalidation or a separate tesst set to get an unbiased estimate of the test set error.

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Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings by Zhenan Sun, Shiguang Shan, Haifeng Sang, Jie Zhou, Yunhong Wang, Weiqi Yuan


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