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Identification of Untrained Facial Image in Combined Global and Local Preserving Feature Space
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International Journal of Biometrics and Bioinformatics (IJBB)
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Volume:  4    Issue:  1
Pages:  1-12
Publication Date:   March 2010
ISSN (Online): 1985-2347
Pages 
1 - 12
Author(s)  
 
Published Date   
31-03-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
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Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Biometric Technology, Face Recognition, Adaptive clustering, Global Feature, Local Feature 
 
 
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In real time applications, biometric authentication has been widely regarded as the most foolproof - or at least the hardest to forge or spoof. Several research works on face recognition based on appearance, features like intensity, color, textures or shape have been done over the last decade. In those works, mostly the classification is achieved by using the similarity measurement techniques that find the minimum distance among the training and testing feature set. When presenting This leads to the wrong classification when presenting the untrained image or unknown image, since the classification process locates at least one wining cluster that having minimum distance or maximum variance among the existing clusters. But for the real time security related applications, these new facial image should be reported and the necessary action has to be taken accordingly. In this paper we propose the following two techniques for this purpose: i. Uses a threshold value calculated by finding the average of the minimum matching distances of the wrong classifications encountered during the training phase. ii. Uses the fact that the wrong classification increases the ratio of within-class distance and between-class distance. Experiments have been conducted using the ORL facial database and a fair comparison is made with these two techniques to show the efficiency of these techniques. 
 
 
 
1 W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, ‘Face Recognition: A Literature Survey‘, UMD CAFR, Technical Report, CAR-TR-948, October 2000.
2 M. Turk, A. Pentland, ‘Eigenfaces for recognition’, Journal of Cognitive NeuroScience, vol. 3, pp.71–86, 1991.
3 Constantine Kotropoulos, Ioannis Pitas, ‘Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication‘, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp.735–746, July 2001.
4 Lian Hock Koh, Surendra Ranganath, Y.V.Venkatesh, ‘An integrated automatic face detection and recognition system‘, Pattern Recognition, vol. 35, pp.1259–1273, 2002.
5 Yongsheng Gao, Maylor K. H. Leung, ‘Face recognition using line edge map‘, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 764–779, June 2002.
6 Constantine L. Kotropoulos, Anastasios Tefas, Ioannis Pitas, ‘Frontal face authentication using discriminating grids with morphological feature vectors‘, IEEE Transactions on Multimedia, vol. 2, no. 1, pp.14–26, March 2000.
7 P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection‘, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 19, no. 7, pp.711-720, 1997.
8 M. Belkin, P. Niyogi, ‘Using manifold structure for partially labeled classification‘, Proceedings of Conference on Advances in Neural Information Processing System, 2002.
9 X. He, P. Niyogi, ‘Locality preserving projections‘, Proceedings of Conference on Advances in Neural Information Processing Systems, 2003.
10 X. You, Q. Chen, D. Zhang, P.S.P.Wang, ‘Nontensor-Product-Wavelet-Based Facial Feature Representation‘, in Image Pattern Recognition - Synthesis and Analysis in Biometrics, pp. 207-224, WSP, 2007.
11 F.Y. Shih, C.F. Chuang, and P.S.P. Wang, ‘Performance Comparisons of Facial Expression Recognition in Jaffe Database‘, International Journal of Pattern Recognition and Artificial Intelligence, Vol.22, No.3, pp. 445-459, 2008.
12 Sang-Woong Lee, P.S.P. Wang, S.N.Yanushkevich, and Seong-Whan Lee, ‘Noniterative 3D Face Reconstruction Based On Photometric Stereo‘, International Journal of Pattern Recognition and Artificial Intelligence, Vol.22, No.3, pp.389-410, 2008.
13 Y. Luo, M. L. Gavrilova, P.S.P.Wang, ‘Facial Metamorphosis Using Geometrical Methods for Biometric Applications‘, International Journal of Pattern Recognition and Artificial Intelligence, Vol.22, No.3, pp.555-584, 2008.
14 S. Ioffe, ‘Probabilistic Linear Discriminant Analysis‘, Proceedings of the European Conference on Computer Vision, Vol.4, pp.531-542, 2006.
15 S.J.D. Prince, and J.H. Elder, ‘Probabilistic linear discriminant analysis for inferences about identity‘, Proceedings of the IEEE International Conference on Computer Vision, 2007.
16 K. Ruba Soundar, K. Murugesan, ‘Preserving Global and Local Features – A Combined Approach for Recognizing Face Images‘, International Journal of Pattern Recognition and Artificial Intelligence, Vol.24, issue 1, 2010.
17 K. Ruba Soundar, K. Murugesan, ‘Preserving Global and Local Features for Robust Face Recognition under Various Noisy Environments ‘, International Journal of Image Processing, Vol.3, issue 6, 2009.
18 B. Moghaddam, T. Jebara, and A. Pentland, ‘Bayesian Face Recognition‘, Pattern Recognition, vol. 33, No. 11, pp.1771-1782, November, 2000.
19 S. Srisuk, and W. Kurutach, ‘Face Recognition using a New Texture Representation of Face Images‘, Proceedings of Electrical Engineering Conference, Cha-am, Thailand, pp. 1097- 1102, 06-07 November 2003.
20 A.M. Bazen, G.T.B. Verwaaijen, S.H. Gerez, L.P.J. Veelenturf, and B.J. Van der Zwaag, ‘A correlation-based fingerprint verification system‘, Proceedings of Workshop on Circuits Systems and Signal Processing, pp.205–213, 2000.
21 K. Nandhakumar, Anil K.Jain, ‘Local correlation-based fingerprint matching‘, Proceedings of ICVGIP, Kolkatta, 2004.
 
 
 
 
 
 
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Ruba Soundar Kathavarayan : Colleagues
Murugesan Karuppasamy : Colleagues  
 
 
 
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