By Zhenan Sun, Shiguang Shan, Haifeng Sang, Jie Zhou, Yunhong Wang, Weiqi Yuan
This publication constitutes the refereed court cases of the ninth chinese language convention on Biometric attractiveness, CCBR 2014, held in Shenyang, China, in November 2014. The 60 revised complete papers provided have been rigorously reviewed and chosen from between ninety submissions. The papers concentrate on face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, program and procedure of biometrics, multi-biometrics and data fusion, different biometric acceptance and processing.
Read or Download Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings PDF
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Additional resources for Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings
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3. (3) and solve this equation. 4. (6) to compute deviation Dr which is generated from the r th class, r ∈C . 5. To classify the test sample into the class that has minimum deviation. In other words, if Dq = min Dr ( q, r ∈ C ) , the test sample will be classified into the q th class. e. vector Rt is composed of all entries of Dr in ascending order. Let vt = log( Rt (2) / Rt (1)) . Rt (1) and Rt (2) are the first two smallest entries of Rt . 26 K. Yan, Y. Xu, and J. Zhang 6. After steps 1-5 have been implemented for all test samples, we identify 10 percent of the test samples with the smallest vt and take the mean of vt s of these test samples as u L .