Face recognition using decision fusion of multiple sparse representation-based classifiers - Biao TANG; Wei JIN; Randi FU; Fei GONG (Dianxin kexue, 2018)
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.