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Using Support Vector Machines to Enhance the Performance of Elastic Graph Matching for Frontal Face Authentication
July 2001 (vol. 23 no. 7)
pp. 735-746

Abstract—A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and Support Vector Machines. Both linear and nonlinear Support Vector Machines are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique.

[1] R. Chellappa, C. Wilson, and S. Sirohey, "Human and Machine Recognition of Faces: A Survey," Proc. IEEE, vol. 83, no. 5, pp. 705-740, 1995.
[2] P.J. Phillips, “Matching Pursuit Filters Applied to Face Identification,” IEEE Trans. Image Processing, vol. 7, no. 8, pp. 1,150-1,164, 1998.
[3] M. Lades, J.C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R.P. Wurtz, and W. Konen, “Distortion Invariant Object Recognition in the Dynamic Link Architecture,” IEEE Trans. Computers, vol. 42, no. 3, pp. 300-311, Mar. 1993.
[4] L. Wiskott, “Phantom Faces for Face Analysis,” Pattern Recognition, vol. 30, no. 6, pp. 837-846, 1997.
[5] J. Zhang, Y. Yan, and M. Lades, “Face Recognition: Eigenface, Elastic Matching, and Neural Nets,” Proc. IEEE, vol. 85, no. 9, pp. 1423-1435, Sept. 1997.
[6] M. Kirby and L. Sirovich,“Application of Karhunen-Loève procedure for the characterization of human faces,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103-108, Jan. 1990.
[7] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[8] G.W. Cottrell and M. Fleming, “Face Recognition Using Unsupervised Feature Extraction,” Proc. Int'l Neural Network Conf., vol. 1, pp. 322-325, July 1990.
[9] H. Bourlard and Y. Kamp, “Auto-Association by Multilayer Perceptrons and Singular Value Decomposition,” Biological Cybernetics, vol. 59, pp. 291-294, 1988.
[10] B.S. Manjunath, R. Chellappa, and C. von der Malsburg, “A Feature-Based Approach to Face Recognition,” Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, pp. 373-378, 1992.
[11] L. Wiskott, J.M. Fellous, N. Kruger, and C. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.
[12] R.P. Würtz, “Object Recognition Robust under Translations, Deformations, and Changes in Background,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 769-775, July 1997.
[13] C. Kotropoulos, A. Tefas, and I. Pitas, "Frontal Face Authentication Using Morphological Elastic Graph Matching," IEEE Trans. Image Proc., vol. 9, no. 4, Apr. 2000, pp. 555-560.
[14] C. Kotropoulos, A. Tefas, and I. Pitas, “Morphological Elastic Graph Matching Applied to Frontal Face Authentication under Well-Controlled and Real Conditions,” Pattern Recognition, vol. 13, no.12, pp. 1935-1947, 2000.
[15] P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. London: Prentice-Hall Int'l, 1982.
[16] R.J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches.New York: John Wiley and Sons, 1992.
[17] V.N. Vapnik, Statistical Learning Theory, John Wiley&Sons, 1998.
[18] C. Cortes and V. Vapnik, "Support Vector Networks," Machine Learning, vol. 20, no. 3, Sept. 1995, pp. 1-25.
[19] C.J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 1-47, 1998.
[20] S. Pigeon and L. Vandendrope, “The M2VTS Multimodal Face Database,” Proc. First Int'l Conf. Audio- and Video-Based Biometric Person Authentication, 1997.
[21] D.L. Swets and J. Weng, Using Discriminant Eigenfeatures for Image Retrieval IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 8, pp. 831-836, Aug. 1996.
[22] P.N. Belhumeur, J. Hespanda, and D. Kriegeman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[23] K. Etemad and R. Chellappa, “Discriminant Analysis for Recognition of Human Faces,” Lecture Notes in Computer Science: Audio- and Video-Based Biometric Person Authentication, J. Bigün, G. Chollet, and G. Borgefors, eds., vol. 1206, pp. 127-142, 1997.
[24] B. Duc, S. Fischer, and J. Bigun, "Face Authentication with Gabor Information on Deformable Graphs," IEEE Trans. Image Proc., vol. 8, no. 4, Apr. 1999, pp. 504-516.
[25] N. Krüger, "An Algorithm for the Learning of Weights in Discrimination Functions Using A Priori Constraints," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, July 1997.
[26] G. Yang and T.S. Huang, “Human Face Detection in a Complex Background,” Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1994.
[27] C. Kotropoulos and I. Pitas, “Rule-Based Face Detection in Frontal Views,” Proc. Int'l Conf. Acoustics, Speech and Signal Processing, vol. 4, pp. 2537-2540, 1997.
[28] R. Fletcher, Practical Methods of Optimization. John Wiley and Sons, second ed., 1987.
[29] V.N. Vapnik, Statistical Learning Theory, John Wiley&Sons, 1998.
[30] T. Evgeniou, M. Pontil, and T. Poggio, “Regularization Networks and Support Vector Machines,” Advances in Computational Math., vol. 13, no. 1, pp. 1-50, 2000.
[31] B. Scholkopf, K. Sung, C.J.C. Burges, and F. Girosi, Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers IEEE Trans. Signal Processing, vol. 45, no. 11, pp. 2758-2765, 1999.
[32] E.E. Osuna, R. Freund, and F. Girosi, “Support Vector Machines: Training and Applications,” A.I. memo no. 1602, Massachusetts Inst. of Technology, Cambridge, Mass., 1997.
[33] S. Gunn, “Support Vector Machines for Classification and Regression,” Technical Report MP-TR-98-05, Image Speech and Intelligent Systems Group, Univ. of Southampton, 1998.
[34] C. Kotropoulos, A. Tefas, and I. Pitas, “Frontal Face Authentication Using Discriminating Grids with Morphological Feature Vectors,” IEEE Trans. Multimedia, vol. 2, no. 1, pp. 14-26, Mar. 2000.
[35] S. Pigeon and L. Vandendorpe, “Image-Based Multimodal Face Authentication,” Signal Processing, vol. 69, pp. 59-79, Aug. 1998.

Index Terms:
Face authentication, elastic graph matching, Fisher's discriminant ratio, constrained least-squares optimization, Support Vector Machines.
Citation:
Anastasios Tefas, 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, doi:10.1109/34.935847
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