Home Chinese • Download e-book for kindle: Biometric Recognition: 11th Chinese Conference, CCBR 2016, by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang

Download e-book for kindle: Biometric Recognition: 11th Chinese Conference, CCBR 2016, by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang

By Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao

This booklet constitutes the refereed court cases of the eleventh chinese language convention on Biometric acceptance, CCBR 2016, held in Chengdu, China, in October 2016.

The eighty four revised complete papers awarded during this e-book have been rigorously reviewed and chosen from 138 submissions. The papers specialize in Face acceptance and research; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral Biometrics; Affective Computing; function Extraction and category conception; Anti-Spoofing and privateness; Surveillance; and DNA and rising Biometrics.

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Additional info for Biometric Recognition: 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings

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11, 1109–1135 (2010) 15. : Discriminative deep metric learning for face verification in the wild. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1875–1882. IEEE (2014) 16. : PCCA: a new approach for distance learning from sparse pairwise constraints. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2666–2672. IEEE (2012) 17. : Supervised descent method and its applications to face alignment. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Springer, Heidelberg (2007). 1007/978-3-540-74549-5 87 16. : Face detection by structural models. Image Vis. Comput. 32(10), 790–799 (2014) 17. : Segmentation as selective search for object recognition. In: International Conference on Computer Vision, pp. 1879–1886. IEEE (2011) 18. : Edge boxes: locating object proposals from edges. , Tuytelaars, T. ) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014). 1007/978-3-319-10602-1 26 19. : Multiscale combinatorial grouping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

Before that we align all images with face alignment algorithm Supervised Descent Method (SDM) [17]. For LBP, we divide each image into 10 × 10 non-overlapping blocks with size 10 × 10. For each block we extract a 59-dimensional LBP feature. Lastly we apply LDA to project the 5900dimensional feature into a 100-dimensional feature. For HOG, we also divide each image into 10 × 10 non-overlapping blocks with size 10 × 10. For each block, we choose 5 × 5 cell size and 18 directions. LDA is applied to project get a 100dimensional feature too.

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