Face recognition method with feature fusion and error feedback

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Yeping, Fan^1 ; Yu, Li^1 ; Desheng, Yang^1 ; Tao, Wan^1 ; Dong, Ma^1 ; Dian, Jiao^2 ; Yiting, Cai^3 ; Mengxian, Chen^3

Anhui Jiyuan Software CO. LTD, Hefei, Anhui

230088, China^1

School of Electric Engineering and Automation, Hefei University of Technology, Hefei, Anhui

230009, China^2

State Grid Wenzhou Power Supply Company, Wenzhou, Zhejiang

325000, China^3

关键词: Calculation models;    Face recognition algorithms;    Face recognition methods;    Intelligent recognition;    Multi-feature fusion;    Performance based;    Performance indices;    Recognition error;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052100/pdf
DOI  :  10.1088/1757-899X/569/5/052100

来源: IOP

【 摘 要 】

A face recognition method with feature fusion and error feedback is proposed to imitate human cognition process. The intelligent recognition mode with multi-level feedback from global to local and from coarse to fine is implemented. Firstly, based on the idea of feedback control, a multi-level feedback model of face recognition with performance index constraints and supervised learning under finite domain is established. Secondly, based on entropy theory and Stochastic Configuration Networks (SCN), a facial feature space and face image classification network model with multi-feature fusion and sufficient separability under certain feature performance conditions are established from the perspective of information theory. Thirdly, based on entropy theory and error theory, the face recognition error representation and calculation model under certain feature performance conditions is established. Finally, the adjustment index of facial feature performance based on recognition error is constructed, and the face recognition algorithm with feature fusion and error feedback is given out. Test and comparison experiments show that our method is effective and feasible.