Discriminative dictionary learning algorithm based on sample diversity and locality of atoms for face recognition |
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| Affiliation: | 1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454001, China;2. School of Computer Science, Shaanxi Normal University, Xi’an 710119, China;3. School of Computer Science and Technology, Nanjing Normal University, Nanjing 210046, China;4. College of Electronics and Information Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China;1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;2. School of National Security and Counter Terrorism, People''s Public Security University of China, Beijing 100038, China;1. Haian Senior School of Jiangsu Province, Nantong 226600, China;2. College of Physical Education, China University of Mining and Technology, Xuzhou 221000, China |
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| Abstract: | Dictionary learning is one of the most important algorithms for face recognition. However, many dictionary learning algorithms for face recognition have the problems of small sample and weak discriminability. In this paper, a novel discriminative dictionary learning algorithm based on sample diversity and locality of atoms is proposed to solve the problems. The rational sample diversity is implemented by alternative samples and new error model to alleviate the small sample size problem. Moreover, locality can leads to sparsity and strong discriminability. In this paper, to enhance the dictionary discrimination and to reduce the influence of noise, the graph Laplacian matrix of atoms is used to keep the local information of the data. At the same, the relational theory is presented. A large number of experiments prove that the proposed algorithm can achieve more high performance than some state-of-the-art algorithms. |
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| Keywords: | Dictionary learning Face recognition Locality constrained Sample diversity |
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