Abstract: The solving process of sparse representation classifier(SRC) is relatively complicated and costs a long time,collaborative representation classifier(CRC) treats all the training samples as the dictionary of unknown samples and the dictionary is large without considering the label information,linear regression classifier(LRC) does not take the differences between inter-class samples into account and ignores the distance information and the neighborhood relations between samples.To address the problems and shortcomings in these representation learning based classification algorithms,this paper proposes a locality regularized double linear reconstruction representation classification method(LRDLRRC) for face recognition.Firstly,LRDLRRC calculates the intra-class nearest neighbors of the query sample and uses the intra-class nearest neighbors to linearly reconstruct the query sample.Then the query sample is represented as a linear combination of all the intra-class reconstruction samples,and the representation coefficient is constrained by the reconstruction error between the query sample and the intra-class reconstruction samples.Finally,the Lagrange multiplier method is applied to solve the representation coefficient,and the classification result of the query sample is determined by the ratio between the reconstruction error and the representation coefficient.Experiments on AR,FRGC and FERET datasets show that the proposed algorithm has superior accuracy,time complexity and strong robustness.

Key words: Double linear reconstruction, Face recognition, Locality regularized, Representation learning