An Incremental SRC Method for Face Recognition

Abstract

Face recognition has been studied for decades and been used widely in our daily life. However, when the practical application is concerned, not only the occlusion, pose and expression variations, but also the increasing training cost caused by the increasing number of training samples are problems we need to solve. In the paper we present a novel incremental SRC method aimed at solving the practical face recognition problems. On one hand, we divide the face into several components, select out the components affected greatly by face variations and abandon these components, the rest parts are used to rebuild the global face which contributes to the final result. On the other hand, inspired by the strategy of “Divide and Rule”, we divide the training samples into multiple groups and train in each group respectively. Therefore, when new training sample is added, we only need to update the model of the group to which the new sample is added, which can greatly decrease the retraining cost.

Numerous experiments are made on the AR and ORL face databases. Experimental results show that the performances of our method outperform the state-of-art linear representation algorithms. In the practical situation of single training sample, our method shows greater advantage than other methods.