Face recognition system based on orthogonal polynomials

被引:0
作者
Krishnamoorthy, R. [1 ]
Bhavani, R. [1 ]
机构
[1] Department of Computer Science and Engineering, Annamalai University
关键词
Edge extraction; Face recognition; operators; Orthogonal polynomials; Point-spread;
D O I
10.3923/jas.2007.109.114
中图分类号
学科分类号
摘要
A new computational model based face recognition system with edge extraction scheme is presented in this research. The proposed model has been built centering on some simple point spread operators, which are easily constructed from a set of orthogonal polynomials. One speciality of these point-spread operators is that they can be used in transforming vis-à-vis approximating 2D monochrome image regions. Also a complete set of difference operators are configured from these point-spread operators. Initially, we detect the face from the given input image using an edge extraction scheme, derived as maximizing the signal to noise ratio due to operator's response supported by the proposed orthogonal polynomials. Simple procedures are derived to compute characteristic subsets of coefficients of the proposed transformation that represent important features, are considered for face recognition on the face detected input image. The prposed face recognition system is tested with the Yale database and also compared with Discrete Cosine Transform based face recognition system, Principle Component Analysis based face recognition system and fisher face recognition system. © 2007 Asian Network for Scientific Information.
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页码:109 / 114
页数:5
相关论文
共 19 条
[1]  
Belhumeur P.N., Hespanha J.P., Kriegman D.J., Eigen-faces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Aanlysis and Machine Intelligence, 19, pp. 711-720, (1997)
[2]  
Brunelli R., Poggio T., Face recognition: Feature Vs Templates, IEEE Trans. Pattern Analysis and Machine Intelligence, 15, pp. 1042-1052, (1993)
[3]  
Canny J., A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 679-698, (1986)
[4]  
Chellappa R., Wilson C.L., Sirohey S., Human and machine recognition of faces: A survey, Proceedings of IEEE, 8, pp. 705-740, (1995)
[5]  
Fisher R.A., Yates F., Statistical Tables for Biological, Agricultural and Medical Research, (1997)
[6]  
Flemming M., Cottrell G., Categorisation of faces using unsupervised feature Extraction, Proceedings of IEEE IJCNN International Joint Conference on Neural Networks, pp. 322-325, (1990)
[7]  
Ganesan L., Bhattacharyya P., A statistical design of experiments approach for texture description, Pattern Recognition, 28, pp. 99-105, (1995)
[8]  
Graham D.B., Allinson N.M., Characterizing virtual eigen signatures for general purpose face recognition: From theory to applications, NATO ASI Series F, Computer and System Sciences, 163, pp. 446-456, (1998)
[9]  
Heseltine T., Pears N., Austin J., Chen Z., Face recognition: A comparison of appearance based approaches, Proc. VIIth Digital Image Computing: Techniques and Applications, 1, pp. 59-68, (2003)
[10]  
Kong S., Heo G.J., Abidi B.R., Paik J., Recent advances in visual and infrared face recognition-a review, Computer Vision and Image Understanding, 97, pp. 103-135, (2005)