Get Biometric Recognition: 8th Chinese Conference, CCBR 2013, PDF

By Xun Gong, Jun Luo, Zehua Fu (auth.), Zhenan Sun, Shiguan Shan, Gongping Yang, Jie Zhou, Yunhong Wang, YiLong Yin (eds.)

ISBN-10: 3319029606

ISBN-13: 9783319029603

ISBN-10: 3319029614

ISBN-13: 9783319029610

This e-book constitutes the refereed court cases of the eighth chinese language convention on Biometric popularity, CCBR 2013, held in Jinan, China, in November 2013. The fifty seven revised complete papers provided have been conscientiously reviewed and chosen from between a hundred submissions. The papers tackle the issues in face, fingerprint, palm print, vein biometrics, iris and ocular biometrics, behavioral biometrics and different comparable themes, and give a contribution new rules to investigate and improvement of trustworthy and functional ideas for biometric authentication.

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Extra resources for Biometric Recognition: 8th Chinese Conference, CCBR 2013, Jinan, China, November 16-17, 2013. Proceedings

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Zheng, and C. Yang Spatially-Weighted Sparse Coding The Spatial Weighting Strategy From Eq. (2), we can see that in the RSC approach, the sparse coding is actually weighted pixel-wise. Elements at the diagonal of M correspond to weights resulting from outlier detection, which tend to have small values for pixels corresponding to outliers. However, different locations in a face image may have distinct discriminabilities. Therefore, we propose to incorporate the spatial importance distribution into the sparse coding scheme.

Hou et al. [13] use relative gradients (RG) to restore an illumination compensated image by solving a Possion equation. Zhang et al. [14] proposed Gradient-faces which is more robust to different illumination thanks to no need to estimate reflectance R. Gradient-faces extracts illumination insensitive measure in certain scale while high frequency details are important to classification, so it has also less edge-preserving ability. The Retinex theory motivated by Land [15] is proposed to extract the reflectance component R( x, y ) from the image I ( x, y ) by the illumination-reflectance model.

Experimental results show that the proposed NSR+BNMF method greatly outperforms two single-feature based methods, namely NSR method and BNMF method. Keywords: Face Recognition, Non-negative Sparse Representation, Block Non-negative Matrix Factorization. 1 Introduction Non-negative matrix factorization (NMF) [1][2] is a popular machine learning algorithm for part-based feature extraction and plays an important role in face recognition. NMF aims to conduct non-negative matrix decomposition on the training image matrix V such that V ≈ W H, where W (≥ 0) and H(≥ 0) are called the basis image matrix and the coefficient matrix respectively.

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Biometric Recognition: 8th Chinese Conference, CCBR 2013, Jinan, China, November 16-17, 2013. Proceedings by Xun Gong, Jun Luo, Zehua Fu (auth.), Zhenan Sun, Shiguan Shan, Gongping Yang, Jie Zhou, Yunhong Wang, YiLong Yin (eds.)

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