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Face Recognition Using Face-ARG Matching
December 2005 (vol. 27 no. 12)
pp. 1982-1988
In this paper, we propose a novel line feature-based face recognition algorithm. A face is represented by the Face-ARG model, where all the geometric quantities and the structural information are encoded in an Attributed Relational Graph (ARG) structure, then the partial ARG matching is done for matching Face-ARG's. Experimental results demonstrate that the proposed algorithm is quite robust to various facial expression changes, varying illumination conditions and occlusion, even when a single sample per person is given.
[1] 1982 W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, no. 4, Dec. 2003.[2] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, pp. 71-86, 1991.[3] P.N. Belhumeur, J.P. Hepanha, and D.J. Kriegman, “Eigen Faces versus Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.[4] P.N. Belhumeur and D.J. Kriegman, “What Is the Set of Images of an Object under All Possible Illumination Conditions?” Int'l J. Computer Vision, vol. 28, no. 3, pp. 245-260, July 1998.[5] A.S. Georghiades, P.N. Belhumeur, and D.J. Kriegman, “From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643-660, June 2001.[6] Y. Gao and M.K.H. Leung, “Face Recognition Using Line Edge Map,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 764-779, June 2002.[7] A.M. Martinez, “Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 748-763, June 2002.[8] S. Romdhani, V. Blanz, and T. Vetter, “Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions,” Proc. Seventh European Conf. Computer Vision, May 2002.[9] R. Gross, I. Matthews, and S. Baker, “Appearance Based Face Recognition and Light Fields,” Technical Report CMU-RI-TR-02-20, Robotics Inst., Carnegie Mellon Univ., Aug. 2002.[10] A.M. Martinez and R. Benavente, “The AR Face Database,” CVC Technical Report no. 24, June 1998.[11] A. Leonardis and H. Bischof, “Robust Recognition Using Eigenimages,” Computer Vision and Image Understanding, vol. 78, pp. 99-118, 2000.[12] M. Black and A. Jepson, “Eigentracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation,” Int'l J. Computer Vision, vol. 26, no. 1, pp. 63-84, 1998.[13] B.W. Hwang and S.W. Lee, “Reconstruction of Partially Damaged Face Images Based on a Morphable Model,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 3, pp. 365-372, Mar. 2003.[14] D.D. Lee and H.S. Seung, “Learning the Parts of Objects by Non-Negative Matrix Factorization,” Nature, vol. 401, pp. 788-791, 1999.[15] S.Z. Li, X.W. Hou, H.J. Zhang, and Q.S. Cheng, “Learning Spatially Localized, Part-Based Representation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 207-212, 2001.[16] B.G. Park, K.M. Lee, S.U. Lee, and J.H. Lee, “Recognition of Partially Occluded Objects Using Probabilistic ARG-Based Matching,” Computer Vision and Image Understanding, vol. 90, no. 3, pp. 217-241, June 2003.[17] W. Zhao, R. Chellappa, and P. Phillips, “Subspace Linear Discriminant Analysis for Face Recognition,” Technical Report CAR-TR-914, Center for Automation Research, Univ. of Maryland, College Park, 1999.[18] A. Shashua and T. Riklin-Raviv, “The Quotient Image: Class Based Re-Rendering and Recognition with Varying Illuminations,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, Feb. 2001.[19] T.W. Lee, Independent Component Analysis: Theory and Applications. Boston: Kluwer Academic Publishers, 1998.[20] V. Bruce, E. Hanna, N. Dench, P. Healey, and M. Burton, “The Importance of “Mass” in Line Drawings of Faces,” Applied Cognitive Psychology, vol. 6, pp. 619-628, 1992.[21] B.S. Tjan, W.L. Braje, G.E. Legge, and D.J. Kersten, “Human Efficiency for Recognizing 3-D Objects in Luminance Noise,” Vision Research, vol. 35, no. 21, pp. 3053-3069, 1995.[22] V. Bruce, Recognising Faces. Lawrence Erlbaum Assoc., 1988.[23] I. Roth and V. Bruce, Perception and Representation. Open Univ. Press, 1995.[24] A.M. Martinez, “Recognizing Expression Variant Faces from a Single Sample Image per Class,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003.[25] A.M. Martinez, “Matching Expression Variant Faces,” Vision Research, vol. 43, no. 9, pp. 1047-1060, 2003.[26] R. Nevatia and K.R. Babu, “Line Extraction and Description,” Computer Graphics and Image Processing, vol. 13, no. 1, pp. 250-269, July 1980.[27] Y. Yacoob and L. Davis, “Smiling Faces Are Better for Face Recognition,” Proc. Int'l Conf. Face Recognition and Gesture Analysis, pp. 59-64, 2002.
Index Terms:
Index Terms- ARG matching, face recognition, structural representation, stochastic analysis.
Citation:
Bo-Gun Park, Kyoung-Mu Lee, Sang-Uk Lee, "Face Recognition Using Face-ARG Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1982-1988, Dec. 2005, doi:10.1109/TPAMI.2005.243