Noise-related face image recognition based on double dictionary transform learning

The existing single dictionary learning algorithms are applied to face recognition and achieve satisfactory results. However, their performance is poor when dealing with noisy images and images involving complex variations such as large pose variations and occlusions. In this paper, a novel noise-related face image recognition method based on double dictionary transform learning (DDTL) is proposed. On the one hand, DDTL introduces the L_2,p-norm to remove the redundant information in the dictionary and the noise involved in the training images, which makes the learned dictionary more discriminative. On the other hand, DDTL introduces the analysis dictionary and performs double dictionary transform learning with the synthetic dictionary. This can better reveal the relationship between the samples and the representation coefficients, and improve the accuracy of the learned dictionary and representation coefficients. Besides, DDTL introduces a linear regression term in the model learning process, which can distinguish and expand the differences between classes. Experimental results on six databases show that DDTL is superior to existing methods.