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 awarded during this e-book have been conscientiously reviewed and chosen from 138 submissions. The papers specialise in Face acceptance and research; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral Biometrics; Affective Computing; characteristic Extraction and class idea; 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

Example text

Secondly, implementing and optimizing all the three steps results in a very heavy work, which is a big barrier for researchers who only cares about classification. Motivated by this, we conduct a specialized benchmark study in this paper, which focuses purely on face classification. 5 million patches for two-class face/non-face classification. Results with several baseline algorithms 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.

3, thus ’too hard’ to identify for a shallow net structure (see Fig. 3). Another possible reason is that CascadeCNN in [11] followed a pipeline of alternation between classification net and calibration net; the Non Maximum Suppresion (NMS) is also deployed during the Cascade-CNN training process, in contrast to the FCB evaluation, which only requires classification net. (a) ROC curve on FCB (b) enlarged Fig. 2. Evaluation results on FCB test set, best viewed in color Face Classification: A Specialized Benchmark Study 27 However, even with the best performer CIFAR-10, the performance on FCB is still far from satisfactory as observed from Fig.

6. Three binary classifiers. Fig. 7. Output of ROI binarization transform. Suppose there are Q eye candidates passed the classification of È É the three-stage classifiers, which are denoted as ~e0 ðx0 ; y0 Þ; . ; ~eQÀ1 ðxQÀ1 ; yQÀ1 Þ . 3 Eye Localization by IMED Matching After three-stage classification, È one or more non-eye candidates É maybe also exist. To detect the real eyes from ~e0 ðx0 ; y0 Þ; . ; ~eQÀ1 ðxQÀ1 ; yQÀ1 Þ , template matching is employed which is based on intensity appearance [12].

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