Discriminative transfer learning with sparsity regularization for single-sample face recognition

Discriminant analysis is an important technique for face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single-sample face recognition (SSFR) because there is only a single training sample per person such that the within-class variation of this person cannot be estimated in such scenario. In this paper, we present a new discriminative transfer learning (DTL) approach for SSFR, where discriminant analysis is performed on a multiple-sample generic training set and then transferred into the single-sample gallery set. Specifically, our DTL learns a feature projection to minimize the intra-class variation and maximize the inter-class variation of samples in the training set, and minimize the difference between the generic training set and the gallery set, simultaneously. To make the DTL be robust to outliers and noise, we employ a sparsity regularizer to regularize the DTL and further propose a novel discriminative transfer learning with sparsity regularization (DTLSR) method. Experimental results on three face datasets including the FERET, CAS-PEAL-R1, and real-world LFW datasets are presented to show the efficacy of the proposed methods.