Robust face recognition based on collaborative representation of multi-directional Gabor feature maps

被引：0
|
作者：
Zhang P. [1 ,2 ]
Xu W. [1 ,2 ,3 ]
Wu S. [1 ,2 ]
Jin X. [1 ,2 ]
机构：
[1] School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan
[2] Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan
[3] Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan
来源：
Xu, Wangming (xuwangming@wust.edu.cn) | 1600年 / Central South University of Technology卷 / 51期
基金：
中国国家自然科学基金;
关键词：
Adaptive K nearest neighbor; Collaborative representation; Face recognition; Multi-directional Gabor feature maps;
D O I：
10.11817/j.issn.1672-7207.2020.02.012
中图分类号：
学科分类号：
摘要：
To improve the performance of face recognition based on SRC under conditions with varied illumination, pose and expression, a robust face recognition method based on multi-directional Gabor feature maps (MGFM) and collaborative representation classification(CRC) was proposed. Firstly, the multi-directional and multi-scale Gabor transform were performed on the face image, and the obtained Gabor features with different scales in the same direction were fused. Secondly, the Gist features were extracted for the fused feature maps in each direction. There were two ways which could be adopted to implement face recognition: 1) the Gist features of all directional feature maps of a face image were cascaded without or with adaptively-weighting to form the global feature vector of the face image and the recognition result was obtained based on collaborative representation classifier; 2) the pre-classification results were obtained by combining Gist features in each direction of a face image with collaborative representation classifiers respectively. The scores of the candidate classes were determined using the adaptive K nearest neighbor strategy, and the final recognition result had the highest total score Thirdly, experiments of face recognition were carried out on ORL, Extended Yale B and AR face database, and the proposed method reached the recognition accuracy of 99.8%, 100% and 99.7% respectively and obtained a faster execution speed. The results show that the proposed method can effectively describe the local information of face image using the multi-directional Gabor feature maps(MGFM), and the improved collaborative representation classification algorithm with the adaptive k-nearest neighbor strategy ultimately achieves higher recognition accuracy and execution efficiency. © 2020, Central South University Press. All right reserved.
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页码：377 / 384
页数：7
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