A test sample oriented two-phase discriminative dictionary learning algorithm for face recognition

In the field of face recognition, conventional dictionary learning algorithms mainly focus on reconstructing the training samples and cannot directly associate the learning procedure with the test samples. Thus, they may not well represent the test samples and obtain unsatisfactory classification performance. In addition, though different training samples have various contributions to learn a dictionary, conventional dictionary learning algorithms cannot well exploit these contributions. In order to address these problems, we present a test sample oriented two-phase dictionary learning (TSOTP-DL) algorithm for face recognition. In the first phase of the TSOTP-DL algorithm, we use all training samples to provide a linear representation of the test sample, and select K ``important'' training samples by using the variety of contributions. In the second phase of the TSOTP-DL algorithm, a dictionary is learned for the test sample by using the selected K$ ``important'' training samples. The TSOTP-DL algorithm utilizes the testing sample to select a subset of the training samples for learning a dictionary, which can reduce the influence of noise. Thus, the training samples are refined according to their contributions to the test sample in our algorithm, and it can improve the discriminative ability of the learned dictionary. In order to further improve the discriminative ability of the learned dictionary, a label embedding of atoms is constructed to encourage the same class training samples to have more similar coding coefficients than different classes. Experiment results demonstrate that our proposed algorithm achieves better classification results than some state-of-the-art dictionary learning and sparse coding algorithms on four public face databases.