Supervised within-class-similar discriminative dictionary learning for face recognition |
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| Affiliation: | 1. College of Information Science and Technology, Beijing Normal University, Beijing, China;2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China;3. Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA;1. Department of Biomedical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, Jiangsu 211106, China;2. Department of Radiology, Guangdong Province Traditional Chinese Medical Hospital, Guangzhou 510006, China;1. School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, PR China;2. Department of Electronic Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli City, Taoyuan County 320, Taiwan, ROC;1. Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, PR China;2. Collaborative Innovation Center of Social Safety Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, PR China;3. The 28th Research Institute of China Electronic Technology Group Corporation, Nanjing 210007, PR China;4. State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, PR China |
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| Abstract: | The current study puts forward a supervised within-class-similar discriminative dictionary learning (SCDDL) algorithm for face recognition. Some popular discriminative dictionary learning schemes for recognition tasks always incorporate the linear classification error term into the objective function or make some discriminative restrictions on representation coefficients. In the presented SCDDL algorithm, we propose to directly restrict the representation coefficients to be similar within the same class and simultaneously include the linear classification error term in the supervised dictionary learning scheme to derive a more discriminative dictionary for face recognition. The experimental results on three large well-known face databases suggest that our approach can enhance the fisher ratio of representation coefficients when compared with several dictionary learning algorithms that incorporate linear classifiers. In addition, the learned discriminative dictionary, the large fisher ratio of representation coefficients and the simultaneously learned classifier can improve the recognition rate compared with some state-of-the-art dictionary learning algorithms. |
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| Keywords: | Discriminative dictionary learning Face recognition Linear classification error Within-class scatter |
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