5 February 2016 Effective face recognition using bag of features with additive kernels
Author Affiliations +
J. of Electronic Imaging, 25(1), 013025 (2016). doi:10.1117/1.JEI.25.1.013025
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.
© 2016 SPIE and IS&T
Shicai Yang, George Bebis, Yongjie Chu, Lindu Zhao, "Effective face recognition using bag of features with additive kernels," Journal of Electronic Imaging 25(1), 013025 (5 February 2016). https://doi.org/10.1117/1.JEI.25.1.013025

Facial recognition systems



Image classification

Machine vision

Computer vision technology

Feature extraction

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