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 booklet constitutes the refereed court cases of the eleventh chinese language convention on Biometric attractiveness, CCBR 2016, held in Chengdu, China, in October 2016.

The eighty four revised complete papers provided during this ebook have been conscientiously reviewed and chosen from 138 submissions. The papers specialise in Face reputation 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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Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I-511. IEEE (2001) 5. : FDDB: A benchmark for face detection in unconstrained settings. UMass Amherst Technical Report (2010) 6. : Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci 1, 801–804 (1956) 7. : On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst.

Com Abstract. Face detection evaluation generally involves three steps: block generation, face classification, and post-processing. However, firstly, face detection performance is largely influenced by block generation and post-processing, concealing the performance of face classification core module. 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.

There are 2576 images in the database which are taken under active infrared (IR) illumination. People on the images have different postures and some wear glasses. Table 2. The experimental results on JAFFE database. 90 % Table 3. The experimental results on CASIA-Iirs-Distance database. 26 % (with glasses + without glasses) The experimental results on JAFFE database are shown in the Table 2. The proposed method performance is superior to multi-view eyes localization method [16], and worse than Tang’s [17].

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