Compound dictionary learning based classification method with a novel virtual sample generation Technology for Face Recognition

Abstract

Face recognition has earned its high reputation for many years since its considerable advances. However, it is still faced with the challenge of the small sample size problem. With the inspiration of the axis-symmetrical property of human faces, we propose a novel virtual sample synthesis strategy to address the issue of limited training samples. It is noteworthy that the novel algorithm can produce symmetry-based virtual face images, in which the pixels in symmetric parts of the face image were exchanged. And it is mathematically very tractable and quite easy to implement. In addition, considering the fact that dictionary learning (DL) based classification methods have excellent learning ability, we incorporate the virtual samples to learn virtual dictionary so as to enhance the accuracy of face recognition. Differing from conventional learning algorithms, the proposed method provides new insights into two crucial parts. Firstly, it proposes an originally creative idea and algorithm to automatically generate symmetry-based virtual samples and obtain virtual dictionary. Secondly, the original dictionary and virtual dictionary are integrated to construct the compound dictionary learning based classification in the way of adaptive weighted fusion. In this paper, we take the axis-symmetrical nature of faces into consideration and design a framework to generate compound dictionary, where more satisfactory classification accuracy can be achieved than the original dictionary learning methods, referred as, the locality-constrained and label embedding dictionary learning (LCLE-DL). Moreover, the experimental results demonstrate the superior performance of the proposed method in comparison with state-of-the-art dictionary learning methods.