Browse > Article

An SVM-based Face Verification System Using Multiple Feature Combination and Similarity Space  

김도형 (한국전자통신연구원 인간로봇상호작용연구팀)
윤호섭 (한국전자통신연구원 인간로봇상호작용연구)
이재연 (한국전자통신연구원 인간로봇상호작용연구팀)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.6, 2004 , pp. 808-816 More about this Journal
Abstract
This paper proposes the method of implementation of practical online face verification system based on multiple feature combination and a similarity space. The main issue in face verification is to deal with the variability in appearance. It seems difficult to solve this issue by using a single feature. Therefore, combination of mutually complementary features is necessary to cope with various changes in appearance. From this point of view, we describe the feature extraction approaches based on multiple principal component analysis and edge distribution. These features are projected on a new intra-person/extra-person similarity space that consists of several simple similarity measures, and are finally evaluated by a support vector machine. From the experiments on a realistic and large database, an equal error rate of 0.029 is achieved, which is a sufficiently practical level for many real- world applications.
Keywords
Multiple Principal Component Analysis; edge distribution; similarity space; Support Vector Machine;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Chellappa, CL. Wilson, and S. Sirohey, 'Human and machine recognition of faces: a survey,' Proceedings cf the IEEE, vol.83, no.5, pp.705-741, May 1995   DOI   ScienceOn
2 Viisage Technology, http://www.viisage.com/facialrecog.htm
3 ZN Vision Technologies AG, httpt://www.zn-ag.com
4 K. Etemad and R. Chellappa, 'Discriminant Analysis for Recognition of Human Face Images,' J. Optical Society of America, vol.14, pp.1724-1733, 1997   DOI   ScienceOn
5 R. Brunelli, and T. Poggio, 'Face recognition: features versus templates,' IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.15, no.10, pp.1042-1052, Oct 1993   DOI   ScienceOn
6 Visionics Corporation, http://www.faceit.com
7 A. Nikolaidis, and I. Pitas, 'Facial feature extraction and pose determination,' Pattern Recognition, vol.33, no.11 , pp,1783-1791, Nov. 2000   DOI   ScienceOn
8 P.S. Penev and J.J. Atick, 'Local Feature Analysis : A General Statistical Theory for Object Representation,' Computation in Neural Systems, vol.7, No.3, pp.477-500, 1996   DOI   ScienceOn
9 M. Turk, and A. Pentland, 'Eigenfaces for Recognition,' Journal of Cognitive Neuroscience, vol.3, no.1, pp.71-86, March 1991   DOI   ScienceOn
10 L. Wiskott, JM. Fellous, N. Kruger and C. Malsburg, 'Face Recognition by Elastic Graph Matching,' IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.19, no.7, pp.775-779, 1996   DOI   ScienceOn
11 Yuchun Fang, Tieniu Tan and Yunhong Wang, 'Fusion of Global and Local Features for Face Verification,' Int'l Conf. Pattern Recognition, vol.2, pp.382-385, 2002   DOI
12 B. Moghaddam, and A. Pentland, 'Probabilistic visual learning for object representation,' IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. 7, pp. 696-710, July 1997   DOI   ScienceOn
13 P. Kalocsai, C. Malsburg and J. Horn, 'Face Recognition by Statistical Analysis of Feature Detectors,' Image and Vision Computing, vol.18, pp.273-278, 2000   DOI   ScienceOn
14 Jie Zhou, 'Face Recognition by Combining Serveral Algorithms,' Int'l Conf. Pattern Recognition, vol.3, pp.497-500, 2002   DOI
15 J. Kittler, J. Matas, K. Johnson, and M. U. Ramos Snchez, 'Combining evidence in personal identity verification systems,' Pattern Recognition Letters, vol.18, pp.845-852, 1997   DOI   ScienceOn
16 CJC. Burges, 'A tutorial on support vector machines for pattern recognition,' Data Mining & Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998   DOI   ScienceOn
17 H. Moon and J. Phillips, 'Analysis of pea-based face recognition algorithms,' Empirical Evaluation Techniques in Computer Vision. IEEE Computer Society Press, 1998
18 P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, 'The FERET database and evaluation procedure for face recognition,' Image and Vision Computing Journal, vol. 16, no. 5, pp. 295-306, 1998   DOI   ScienceOn
19 Wendy S. Yambor, 'Analysis of PeA-based and Fisher Discriminant-based Image Recognition Algorithms,' Technical Report CS-00-103, Dept.of Computer Science, Colorado State University, 2000