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 e-book constitutes the refereed complaints of the eleventh chinese language convention on Biometric popularity, CCBR 2016, held in Chengdu, China, in October 2016.

The eighty four revised complete papers offered during this e-book have been conscientiously reviewed and chosen from 138 submissions. The papers concentrate on Face attractiveness and research; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral Biometrics; Affective Computing; characteristic Extraction and category thought; Anti-Spoofing and privateness; Surveillance; and DNA and rising Biometrics.

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

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Qin et al. Binary Classifiers Although it is efficient and reliable to detect eye candidates by FRST, binarization and region generation, it also remains a great challenge to identify the eye(s) accurately from the candidates. Because the candidates may also be nostrils, nevus, node pads of glasses etc. Three-stage binary classifiers are designed to further exclude unreliable eye candidates. The binary classifiers are denoted as Ck ðwk ; hk Þ; k 2 f0; 1; 2g shown in Fig. 6, where wk and hk are respectively the width and height of classifier Ck .

In: Proceedings of the 24th International Conference on Machine Learning, pp. 209–216. ACM (2007) 14. : Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 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.

We trained a 4-anchor RPN network with some slight modifications on the fc6 and fc7 InnerProduct layer of Zeiler and Fergus model [12]. The default anchor ratio was set to 1:1 and we compute anchors at 4 different scales (2,8,16,32). During training, the number of categories are modified to 2 (face and background) with 2 * 4 bounding box coordinates to be predicted. Softmax and smoothL1 loss are deployed for training classification and bounding box prediction respectively. io/index/ccbr 2016 24 J. Duan et al.

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