Biometric Recognition: 9th Chinese Conference, CCBR 2014, by Zhenan Sun, Shiguang Shan, Haifeng Sang, Jie Zhou, Yunhong

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 reputation, 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 specialize in face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, software and method of biometrics, multi-biometrics and data fusion, different biometric acceptance and processing.

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Additional info for Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9, 2014. Proceedings

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It indeed breaks the bottleneck on real-world applications of previous TPSR method and makes it become a feasible method. The experimental results show that the proposed ITPSR can obtain excellent performance. Moreover, other representation methods almost have parameters that need to be manually set, whereas the proposed ITPSR does not need to do so and can automatically set a good value for the parameter. Automatic Two Phase Sparse Representation Method 29 References 1. : Robust Face Recognition via Sparse Representation.

Keywords: Face recognition, LDA, LPP, Feature extraction, Parameter determination. 1 Introduction Facial feature extraction is a crucial problem for Face Recognition (FR). Based on different feature extraction criteria, a large number of FR algorithms have been developed during the past decades. The feature extraction methods are mainly divided into two categories, namely the global feature extraction and the local feature extraction. Linear discriminant analysis (LDA) [1], which depicts the global feature, is a representative method of the first category.

3 The Proposed Method The main steps of the proposed method are described as follows: 1. To initialize Μ as Μ 0 . 2. To use the first phase to determine Μ nearest neighbors for the test sample. 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.

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