Classic Methods of Face Recognition

Abstract:

Over the past few years, face recognition has been actively studied. Some classic methods of face recognition are introduced in this paper. At first, we give an overview of face recognition and its applications. Then, we will present some classic techniques of face recognition. A brief overview of ORL face database which is usually used to test the performance of these methods is given. At last, we will give a summary of the research results.

• Periodical: Advanced Materials Research (Volumes 121-122)
• Pages: 350-353
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.121-122.350
• Online since: June 2010
• Authors: Hai Yang Zhang, Xian Wei Li
• Keywords: Eigenceface, Face Recognition, HMM, Support Vector Machine (SVM)

References

[1] W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 2003, pp.399-458.

DOI: 10.1145/954339.954342

[2] Adini, Y., Moses, Y., Ullman, S., 1997. Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Machine Intell. 19 (7), 721-732.

DOI: 10.1109/34.598229

[3] M.A. Turk, A.P. Pentland, Face Recognition Using Eigenfaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3-6 June 1991, Maui, Hawaii, USA, pp.586-591.

DOI: 10.1109/cvpr.1991.139758

[4] L. Sirovich and M. Kirby, Low-Dimensional procedure for the characterisation of human faces, J. Optical Soc. of Am., vol. 4, pp.519-524, (1987).

[5] M. Kirby and L. Sirovich, Application of the Karhunen-Loève procedure for the characterisation of human faces, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, pp.831-835, Dec. (1990).

DOI: 10.1109/34.41390

[6] M.A. Grudin, A compact multi-level model for the recognition of facial images, Ph.D. thesis, Liverpool John Moores Univ., (1997).

[7] R. Gottumukkal and V.K. Asari, An improved face recognition technique based on modular PCA approach, Pattern Recognition. Lett. 25 (2004) (4), pp.429-436.

DOI: 10.1016/j.patrec.2003.11.005

[8] T.J. Stonham, Practical face recognition and verification with WISARD, Aspects of Face Processing, pp.426-441, (1984).

DOI: 10.1007/978-94-009-4420-6_44

[9] K.K. Sung and T. Poggio, Learning human face detection in cluttered scenes, Computer Analysis of Image and patterns, pp.432-439, (1995).

DOI: 10.1007/3-540-60268-2_326

[10] L. Rabiner and B. Huang, Foundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, (1993).

[11] F. Samaria and F. Fallside, Face identification and feature extraction using hidden markov models, Image Processing: Theory and Application, G. Vernazza, ed., Elsevier, (1993).

[12] V.N. Vapnik, The nature of statistical learning theory, New York: Springverlag, (1995).

[13] F. Samaria and S. Young, HMM based architecture for face identification, ', Image and Computer Vision, vol. 12, pp.537-583, October (1994).

DOI: 10.1016/0262-8856(94)90007-8

[14] Xu Jiwei, Wang Zhanfeng, Feng Yanhong, Li Guangxia, A Face Recognition Method Based on Fuzzy Neural Networks, Journal of Tangshan Teachers College, vo1. 30 No. 2, pp.71-73, March (2008).

[15] Guodong Guo , Stan Z Li , Kap Luk Chan1 Support vector machines for face recognition [J ] 1 Image and Vision Computing , 2001 , 19 (9) : 631～638.

DOI: 10.1016/s0262-8856(01)00046-4