Face recognition using point symmetry distance-based RBF network |
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| Affiliation: | 1. Instituto de Telecomunicações, University of Beira Interior, Portugal;2. Department of Mathematics and Informatics, UCAD, Senegal;3. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China;1. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China.;2. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;3. Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan;4. Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Tainan, Taiwan;5. Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan;1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China;2. Center for Breast, People''s Hospital of Peking University, Beijing, China;1. Department of Management, London School of Economics and Political Science, UK;2. Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France;1. Thayer School of Engineering, Dartmouth College, 14 Engineering Dr., Hanover, NH 03755;2. Department of Diagnostic Radiology, Dartmouth Medical School, Lebanon, NH;3. Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi''an, Shannxi, China |
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| Abstract: | In this paper, a face recognition technique using a radial basis function neural network (RBFNN) is presented. The centers of the hidden layer units of the RBFNN are selected by using a heuristic approach and point symmetry distance as similarity measure. The performance of the present method has been evaluated using the AT&T Laboratories Cambridge database (formerly called ORL face database) and compared with some other methods, which use the same database. The evaluation has been done using two methodologies; first with no rejection criteria, and then with rejection criteria. The experimental results show that the present method achieves excellent performance, both in terms of recognition rates and learning efficiency. The average recognition rates, as obtained using 10 different permutations of 1, 3 and 5 training images per subject are 76.06, 92.61 and 97.20%, respectively, when tested without any rejection criteria. On the other hand, by imposing rejection criteria, the average recognition rates of the system become 99.34, 99.80 and 99.93%, respectively, for the above permutations of the training images. The system recognizes a face within about 22 ms on a low-cost computing system with a 450 MHz P-III processor, and thereby extending its capability to identify faces in interframe periods of video and in real time. |
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