Face recognition method based on sparse representation and feature fusion

In order to improve the accuracy of face recognition, a face recognition algorithm based on sparse representation and feature fusion is proposed. Firstly, the training samples and test samples are pre-processed by gray image conversion, scale scaling, histogram equalization, smooth filtering and so...

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Bibliographic Details
Main Authors: Jiang, Changjiang, Wang, Mingyi, Tang, Xianlun, Mao, Rong
Format: Conference Proceeding
Language:English
Subjects:
Encoding
Face
Face recognition
Feature extraction
feature fusion
Image recognition
Robustness
sparse representation
Training
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Summary:In order to improve the accuracy of face recognition, a face recognition algorithm based on sparse representation and feature fusion is proposed. Firstly, the training samples and test samples are pre-processed by gray image conversion, scale scaling, histogram equalization, smooth filtering and so on, and the LBP, Gabor and HOG features of face images are extracted. And then the RSC classification test is carried on the partial samples. A loss function is defined according to the recognition result and the classification residual, then the weight vector is obtained by using the regularized least square method to minimize the loss function. Finally, the final residual is calculated according to the weight vector so as to obtain the final classification result. The experimental results on AR face dataset and LFW face dataset show that the recognition rate of our algorithm is obviously higher than the single feature recognition method, and it is robust to illumination, occlusion and expression.
ISSN:2688-0938
DOI:10.1109/CAC48633.2019.8997456