ITM Web Conf.
Volume 7, 20163rd Annual International Conference on Information Technology and Applications (ITA 2016)
|Number of page(s)||9|
|Section||Session 2: Signal and Image Processing|
|Published online||21 November 2016|
Robust Face Recognition Via Gabor Feature and Sparse Representation
College of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture, Beijing, China
a Corresponding author Yu-juan HAO. E-mail address: firstname.lastname@example.org
Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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