Telecommunications Science ›› 2018, Vol. 34 ›› Issue (4): 31-40.doi: 10.11959/j.issn.1000-0801.2018010

• research and development • Previous Articles     Next Articles

Face recognition using decision fusion of multiple sparse representation-based classifiers

Biao TANG,Wei JIN,Randi FU,Fei GONG   

  1. Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo 315211,China
  • Revised:2017-12-14 Online:2018-04-01 Published:2018-05-02
  • Supported by:
    The National Natural Science Foundation of China(61471212);The Natural Science Foundation of Zhejiang Province of China(LY16F010001);The Natural Science Foundation of Ningbo of China(2016A610091);The Natural Science Foundation of Ningbo of China(2017A610297)

Abstract:

A new approach to face recognition combining decision fusion and multiple sparse representation-based classifiers was proposed to improve the robustness of the traditional methods.Different types of facial features were extracted,followed by training multiple sparse representation sub-classifiers,and then decision fusion was used to obtain the recognition result of the system.The significant advantage of the proposed scheme lines in that the final recognition results were not driven by averaging outputs of multiple sub-classifiers,but driven by combining multiple outputs via weighted fusion method.In particular,the fusion weights were adaptively determined by an iterative pro-cedure according to the different classification performance of each sub-classifier.Extensive experiments on Yale B,JAFFE and AR face databases demonstrate that the proposed approach is much more effective than state-of-the-art methods in dealing with lighting changes,expression changes and face occlusion and multi factor mixed interference.

Key words: face recognition, sparse representation-based classifier, decision fusion

CLC Number: 

No Suggested Reading articles found!