Paper title: Nonlinear Fusion of Colors to Face Authentication Using EFM Method
Published in: Issue 3, (Vol. 4) / 2010Download
Publishing date: 2010-10-26
Pages: 42-50
Author(s): FEDIAS M. , SAIGAA D.
Abstract. The authentication systems of face generally used the grayscale face image as input, but in this paper we studied the contribution of the color to the authentication system of face. For the extraction of face characteristics for the data base, we tested different spaces colors on the Enhanced Fisher linear discriminant Model (EFM) which is presented as an alternative features extraction algorithm to Principal Component Analysis (PCA) widely used in automatic face recognition. And once the characteristic vector is extracted, the next stage consists of comparing it with the vector characteristic of face which is authenticated, and with the use of each component color alone at the input of this system, we calculated the error rates in the two sets of validation and test for the data base XM2VTS according to the protocol of Lausanne. Finally, the results obtained in different spaces or components colorimetric are combined by the use of a nonlinear fusion with a simple neuron network MLP (Multi layer perceptron), the results obtained confirm the efficient of color to improve the performance of an authentication system of face.
Keywords: Principal Components Analysis PCA, Enhanced Fisher Linear Discriminant Model (EFM), Face Authentication, Fisher Linear Discriminant (FLD), Color Spaces, MLP
References:

1. TURK M. A. et PENTLAND A. P.: Face recognition using eigenfaces. IEEE Comput. ScoPress, p. 586-591, June 1991.

2. Matthew Turk et Alex Pentland : Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1):71–86, 1991

3. Wendy S. Yambor, Analysis of PCA-based and fisher discriminantbased image recognition algorithms, Technical Report CS-00-103, july 2000.

4. P. Belhumeur, J.P. Hespanha, D.J. Kriegman , Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, pp. 711-720.

5. M. Fedias , D. Saigaa : L'apport de la couleur a l'authentification de visage, Conférence internationale a constantine JIG'2007 proceedings, pp. 120-126.

6. Chengjun Liu and Harry Wechsler “Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition”, IEEE Trans. Image Processing, vol. 11, no. 4, pp. 467- 476, 2002.

7. D. L. Swets and J. Weng, “Using discriminant eigenfeatures for image retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831– 836, 1996.

8. C. Liu and H.Wechsler, “Robust coding schemes for indexing and retrieval from large face databases,” IEEE Trans. on Image Processing, vol. 9, no. 1, pp. 132–137, 2000.

9. K. Messer, J. Matas, J. Kittler et K. Jonsson : Xm2vtsdb : The extended m2vts database. Audio- and Video-based Biometric Person Authentication (AVBPA), pages 72–77, Mars 1999.

10. J. Luettin and G. Maitre. “Evaluation protocol for the extended M2VTS database”. IDIAP, available at: http://www.ee.surrey.ac.uk/Research /VSSP/xm2vtsdb/faceavbpa2001/protocol.ps, 1998.

11. Claude TOUZET, cours : Les réseaux de neurones artificiels, introduction au connexionnisme,1992.

12. Nadine St-Amand, cours : Réseaux neuronaux et classification (Fonctionnement, structure, initialisation et entraînement).

13. K. Etemad and R. Chellappa, “Discriminant analysis for recognition of human face images,”

14. J. Opt. Soc. Am. A, vol. 14, pp. 1724– 1733, 1997.

15. D. Saigaa, K. Bemahammed, S. Lelandais and N. Benoudjit “Random Pulling Model (RPM) For Face Authentication”, Asian Journal of Information Technology, ISSN 1682-3915, Vol. 5, Issue 3, Mars 2006, pp. 285-289 .

16. D. Saigaa, S. Lelandais, K. Bemahammed and N. Benoudjit “ Improvements for face authentication using color information”, WSEAS Transactions on Signal Processing, ISSN 1790-5022, Vol. 2, March 2006, pp. 343-350.

17. D. Saigaa, N. Benoudjit, K. Bemahammed and S. Lelandaius “Face Authentication using Enhanced Fisher linear discriminant Model (EFM)”, WSEAS International. Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (CIMMACS’05), in Miami Florida (USA), Nov. 17-19, 2005, pp. 155-160

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