Sample group and misplaced atom dictionary learning for face recognition

Authors : MENG WANG, ZHENGPING HU, ZHE SUN, MEI ZHU, MEI SUN

Pages : 4421-4430

View : 12 | Download : 9

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Latest research results have demonstrated the effectiveness of both sparse insert ignore into journalissuearticles values(or collaborative); representation and dictionary learning for problem solving in face recognition and other signal classification. Considering the fact that an informative dictionary helps a lot in sparse coding, a novel model that consists of group dictionary learning and high-quality joint kernel collaborative representation was proposed in this paper, where rich information from original and virtual space was mined and constructed as a sample group space to improve classification accuracy. Meanwhile, joint kernel collaborative representation with an $\ell_{2}$-regularization-based classifier was used to capture more nonlinear structure and minimize the time cost. Experiments showed that the proposed method outperformed several similar state-of-the-art methods in terms of accuracy and computational complexity.
Keywords : Sample group, misplaced atom, dictionary learning, joint kernel collaborative representation, face recognition