A joint classification approach via sparse representation for face recognition
Yandong Wen, Youjun Xiang, Yuli Fu · 2014
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.