Sparse representation for robust face recognition by dictionary decomposition |
| |
| Affiliation: | 1. Department of Mathematics, University of Kaiserslautern, Kaiserslautern, Germany;2. Fraunhofer ITWM, Kaiserslautern, Germany;1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, 100093 Beijing, China;2. Zhejiang Wanli University, Ningbo, China;3. University of Thessaly, Volos, Greece;4. University of Nebraska-Lincoln, Omaha, USA;1. Department of Electrical Engineering, Da-Yeh University, 168 University Rd., Dacun, Changhua 515, Taiwan, ROC;2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, 415 Chien Kung Rd., Kaohsiung 807, Taiwan, ROC;3. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, 300 Liu-Ho Rd., Makung, Penghu 880, Taiwan, ROC;1. College of Communication Engineering, Chongqing University, Chongqing 400044, China;2. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;1. Department of Chemistry, University of Winnipeg, Winnipeg, MB, R3B 2E9, Canada;2. Department of Entomology, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada |
| |
| Abstract: | Sparse representation-based classification (SRC) method has gained great success in face recognition due to its encouraging and impressive performance. However, in SRC the data used to train or test are usually corrupted, and hence the performance is affected. This paper proposes a robust face recognition approach by means of learning a class-specific dictionary and a projection matrix. Firstly, the training data are decomposed into class-specific dictionary, non-class-specific dictionary, and sparse error matrix. Secondly, in order to correct the corrupted test data, the data are projected onto their corresponding underlying subspace, and a projection matrix between the original training data and the class-specific dictionary is learned. Then, the features of the class-specific dictionary and the corrected test data are extracted by using Eigenface method. Finally, the SRC is performed to classify. Extensive experiments conducted on publicly available data sets show that the proposed algorithm performs better than some state-of-the-art methods. |
| |
| Keywords: | Sparse representation Face recognition Dictionary decomposition Projection matrix |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
