Zhaoxiang Zhang, Chao Wang, Yunhong Wang (auth.), Zhenan's Biometric Recognition: 6th Chinese Conference, CCBR 2011, PDF

By Zhaoxiang Zhang, Chao Wang, Yunhong Wang (auth.), Zhenan Sun, Jianhuang Lai, Xilin Chen, Tieniu Tan (eds.)

ISBN-10: 3642254489

ISBN-13: 9783642254482

ISBN-10: 3642254497

ISBN-13: 9783642254499

This ebook constitutes the refereed complaints of the sixth chinese language convention on Biometric acceptance, CCBR 2011, held in Beijing, China in December 2011. The 35 revised complete papers have been conscientiously reviewed and chosen from seventy one submissions. The papers are prepared in topical sections on difficulties in face; iris; hand biometrics; speaker; handwriting; gait; behavioral and delicate biometrics; and security.

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We run the experiment 10 times for each L. The average recognition rate and standard deviation (std) across 10 runs of tests of each method are illustrated in Table 1. Table 1. The average rates (%) and std of PCA, FLDA, LBP+Chi2, Gabor+FLDA, LARK+FLDA and ID-LARK under NN classifier using 10 run test on the PIE (pose 09) database Methods/ No. 29 Conclusions In this paper, we develop a novel method image decomposition based on locally adaptive regression kernels for image feature extraction. We first compute dense local structure features of each pixel via LARK.

Sample images of a person under different illuminations in the PIE database (Pose 09) In this experiment, L (L varies from 4 to 12 with 2 interval) images are randomly selected from the gallery images of each individual to form the training set, and the remaining images are used for testing. We run the experiment 10 times for each L. The average recognition rate and standard deviation (std) across 10 runs of tests of each method are illustrated in Table 1. Table 1. The average rates (%) and std of PCA, FLDA, LBP+Chi2, Gabor+FLDA, LARK+FLDA and ID-LARK under NN classifier using 10 run test on the PIE (pose 09) database Methods/ No.

Further, low dimensional and compact ID-LARK features are obtained by using FLDA. The difference between ID-LARK and LARK are also shown in Fig 1 and Fig 2. In addition, we know that LARK describes dense structure information in their local window. In general, the local window size is also set to 5 × 5, 7 × 7 and 9 × 9. However, why we set the local window size to 3 × 3? On account of some significant structure information is distributed to various sub-images when using the larger local window size.

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Biometric Recognition: 6th Chinese Conference, CCBR 2011, Beijing, China, December 3-4, 2011. Proceedings by Zhaoxiang Zhang, Chao Wang, Yunhong Wang (auth.), Zhenan Sun, Jianhuang Lai, Xilin Chen, Tieniu Tan (eds.)


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