Gaussian Mixture Model Based Semi-supervised Sparse Representation for Face Recognition

Sparse representation generally relies on supervised learning, however, the samples in real life are often unlabeled and sparse representation cannot make use of the information of the unlabeled samples. In this paper, we propose a Gaussian Mixture Model based Semi-supervised Sparse Representation (GSSR) for face recognition, and it takes full advantage of unlabeled samples to improve the performance of sparse representation. Firstly, we present a semi-supervised sparse representation, which is a linear additive model with rectification and makes all rectified samples conform to Gaussian distribution. Then, we reconstruct a new dictionary that derived from predicting the labels of unlabeled samples through Expectation-Maximization algorithm. Finally, we use the new dictionary embedded into sparse representation to recognize faces. Experiments on AR, LFW and PIE databases show that our method effectively improves the classification accuracy and has superiority even with a few unlabeled samples.