A joint classification approach via sparse representation for face recognition
Signal Processing(2014)
摘要
We consider the problem of automatically recognizing human faces in which sparse representation-based classification (SRC) offers a key. SRC includes two steps: seeking sparest solution and making decision by dictionary classifier (DC). Aiming at improving the performance of face recognition, this paper proposes a joint classification approach based on sparse representation. We initialize dictionary with part of the training samples and train a linear classifier (LC) with the remaining. Thus, the joint classifier (JC), which combines the DC and LC, can decide which subject the query image belongs to. To validate the joint classifier, a residual-based evaluating criterion is established to measure the classification reliability for two classifiers. Experimental results verify that the proposed joint classification strategy significantly improves recognition accuracy at the cost of affordable computational complexity.
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关键词
computational complexity,face recognition,image classification,image coding,image representation,image retrieval,dc,jc,lc training,src,automatic human face recognition,classification reliability measurement,dictionary classifier,dictionary initialization,face recognition performance improvement,joint classification approach,linear classifier training,query image,recognition accuracy improvement,residual-based evaluation criterion,sparse coding,sparse representation-based classification,joint classification,residual-based criterion,simplified training,sparse representation classification,reliability,dictionaries,databases
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