By Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
ISBN-10: 3319466534
ISBN-13: 9783319466538
ISBN-10: 3319466542
ISBN-13: 9783319466545
This booklet 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 provided during this booklet have been rigorously reviewed and chosen from 138 submissions. The papers specialize in Face attractiveness and research; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral Biometrics; Affective Computing; characteristic Extraction and class concept; 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
Sample text
Our results show that, without the help of post-processing, the performance of face classification itself is still not very satisfactory, even with a powerful CNN method. The data and evaluation code of this study will be released to the public1 to help assess performance of face classification, and ease the participation of related researchers who want to try their algorithms for face detection. With this benchmark, researchers only need to do feature extraction and face classification per image patch, regardless the troubling block generation and post-processing tasks.
As shown in Fig. 1, P þ ve ðPÞ represents the positively-affected pixel location on the positive gradient direction of pixel P. Similarly, PÀve ðPÞ represents the negativelyaffected pixel location on the negative gradient direction of pixel P. The coordinates of P þ ve ðPÞ and PÀve ðPÞ are given by gðPÞ gð P Þ n ; PÀve ðPÞ ¼ P À round n : P þ ve ðPÞ ¼ P þ round kgðPÞk kgðPÞk ð1Þ Where round ðÁÞ denotes the operation of getting the nearest integer of each vector element. g(P) is the gradient vector.
Process. Syst. 2, 849–856 (2002) 8. : Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002) 9. : Clustering by passing messages between data points. Science 315(5814), 972–976 (2007) 10. : Joint cascade face detection and alignment. , Tuytelaars, T. ) ECCV 2014. LNCS, vol. 8694, pp. 109–122. Springer, Heidelberg (2014). 1007/ 978-3-319-10599-4 8 11. : Distance metric learning with application to clustering with side-information.
Biometric Recognition: 11th Chinese Conference, CCBR 2016, Chengdu, China, October 14-16, 2016, Proceedings by Zhisheng You, Jie Zhou, Yunhong Wang, Zhenan Sun, Shiguang Shan, Weishi Zheng, Jianjiang Feng, Qijun Zhao
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