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Effective face recognition using bag of features with additive kernels

[+] Author Affiliations
Shicai Yang, Yongjie Chu, Lindu Zhao

Southeast University, Institute of Systems Engineering, No. 2 Sipailou Road, Nanjing 210096, China

George Bebis

University of Nevada, Department of Computer Science and Engineering, Reno, 1664 North Virginia Street, Nevada 89557, United States

J. Electron. Imaging. 25(1), 013025 (Feb 05, 2016). doi:10.1117/1.JEI.25.1.013025
History: Received July 20, 2015; Accepted January 12, 2016
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Abstract.  In past decades, many techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the same category. Each category and each face image could be both represented by a simple pyramid histogram. Spatial dense scale-invariant feature transform features and bag of features method are used to build categories and face representations. In an effort to make the method more efficient, a linear support vector machine solver, Pegasos, is used for the classification in the kernel space with additive kernels instead of nonlinear SVMs. Our experimental results demonstrate that the proposed method can achieve very high recognition accuracy on the ORL, YALE, and FERET databases.

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Citation

Shicai Yang ; George Bebis ; Yongjie Chu and Lindu Zhao
"Effective face recognition using bag of features with additive kernels", J. Electron. Imaging. 25(1), 013025 (Feb 05, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.1.013025


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