By Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
This publication constitutes the refereed court cases of the eleventh chinese language convention on Biometric popularity, CCBR 2016, held in Chengdu, China, in October 2016.
The eighty four revised complete papers awarded during this publication have been conscientiously reviewed and chosen from 138 submissions. The papers concentrate on Face reputation and research; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral Biometrics; Affective Computing; function Extraction and category thought; Anti-Spoofing and privateness; Surveillance; and DNA and rising Biometrics.
Read or Download Biometric Recognition: 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings PDF
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Extra info for Biometric Recognition: 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings
CCBR 2016, LNCS 9967, pp. 22–29, 2016. ). Today, popular face detection benchmarks such as AFW , FDDB , and WIDER FACE  still continue to use such evaluation procedure. However, on one hand, the performance of face detection methods is largely inﬂuenced by speciﬁc settings of block generation and post-processing, therefore, it is not easy to know the speciﬁc performance of the core module, namely the face classiﬁcation part in existing methods. g. feature and classiﬁcation researchers) in introducing their ideas for face detection.
4 Conclusion Face detection generally involves three steps with face classiﬁcation being its core module. However, it is not easy to determine the actual performance of the face classiﬁcation part due to the large inﬂuence of block generation and postprocessing in traditional benchmarks. Motivated by this, we conduct a specialized benchmark study in this paper, which focuses purely on face classiﬁcation. 5 millions of face and non-face sample patches, and build a benchmark dataset (FCB) for two-class face classiﬁcation evaluation.
3, pp. 519–522 (1996) 7. : Regression and classiﬁcation approaches to eye localization in face images. In: Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, pp. 441–448 (2006) 8. : Automatic eye detection and its validation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 3, pp. 164–172 (2005) 9. : Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995) Binary Classiﬁers and Radial Symmetry Transform 39 10.