Subscribe
Issue No.03 - March (2013 vol.35)
pp: 728-739
O. Ocegueda , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
Tianhong Fang , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
S. K. Shah , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
I. A. Kakadiaris , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
ABSTRACT
We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being “discriminative” or “nondiscriminative” for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.
INDEX TERMS
Face, Three dimensional displays, Vectors, Face recognition, Image segmentation, Geometry, Algorithm design and analysis, face and gesture recognition, Feature evaluation and selection, object recognition, Markov random fields, segmentation, image processing and computer vision, pattern recognition
CITATION
O. Ocegueda, Tianhong Fang, S. K. Shah, I. A. Kakadiaris, "3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 3, pp. 728-739, March 2013, doi:10.1109/TPAMI.2012.126
REFERENCES
 [1] L. Yin, X. Chen, Y. Sun, T. Worm, and M. Reale, "A High-Resolution 3D Dynamic Facial Expression Database," Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 1-6, Sept. 2008. [2] H. Tang and T. Huang, "3D Facial Expression Recognition Based on Automatically Selected Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, pp. 1-8, June 2008. [3] F. Tsalakanidou and S. Malassiotis, "Real-Time 2D+3D Facial Action and Expression Recognition," Pattern Recognition, vol. 43, no. 5, pp. 1763-1775, May 2010. [4] G. Toderici, S. O$^\prime$ Malley, G. Passalis, T. Theoharis, and I.A. Kakadiaris, "Ethnicity- and Gender-Based Subject Retrieval Using 3D Face-Recognition Techniques," Int'l J. Computer Vision, special issue on 3D object retrieval, vol. 89, nos. 2/3, pp. 382-391, Sept. 2010. [5] A. Martinez, "Deciphering the Face," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, pp. 7-12, June 2011. [6] K. Etemad and R. Chellappa, "Discriminant Analysis for Recognition of Human Face Images," J. Optical Soc. of Am. A, vol. 14, no. 8, pp. 1724-1733, Aug. 1997. [7] F. Daniyal, P. Nair, and A. Cavallaro, "Compact Signatures for 3D Face Recognition under Varying Expressions," Proc. IEEE Int'l Conf. Advanced Video and Signal Based Surveillance, pp. 302-307, Sept. 2009. [8] M. Savvides, R. Abiantun, J. Heo, S. Park, C. Xie, and B. Vijayakumar, "Partial and Holistic Face Recognition on FRGC-II Data Using Support Vector Machine Kernel Correlation Feature Analysis," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop, pp. 48-53, June 2006. [9] T. Faltemier, K. Bowyer, and P. Flynn, "A Region Ensemble for 3D Face Recognition," IEEE Trans. Information Forensics and Security, vol. 3, no. 1, pp. 62-73, Mar. 2008. [10] I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," J. Machine Learning Research, vol. 3, pp. 1157-1182, Mar. 2003. [11] G.H. John, R. Kohavi, and K. Pfleger, "Irrelevant Features and the Subset Selection Problem," Proc. 11th Int'l Conf. Machine Learning, pp. 121-129, July 1994. [12] Z. Zhao and H. Liu, "Searching for Interacting Features," Proc. 20th Int'l Joint Conf. Artificial Intelligence, pp. 1156-1161, Jan. 2007. [13] H. Liu, H. Motoda, R. Setiono, and Z. Zhao, "Feature Selection: An Ever Evolving Frontier in Data Mining," Proc. Fourth Workshop Feature Selection in Data Mining, vol. 4, pp. 4-13, June 2010. [14] M. Rivera and J.C. Gee, "Two-Level MRF Models for Image Restoration and Segmentation," Proc. British Machine Vision Conf., Sept. 2004. [15] I.A. Kakadiaris, G. Passalis, G. Toderici, M.N. Murtuza, Y. Lu, N. Karampatziakis, and T. Theoharis, "Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 640-649, Apr. 2007. [16] A. Savran, N. Alyüz, H. Dibeklioğlu, O. Çeliktutan, B. Gökberk, B. Sankur, and L. Akarun, "Bosphorus Database for 3D Face Analysis," Proc. First COST 2101 Workshop Biometrics and Identity Management, pp. 47-56, May 2008. [17] A. 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. [18] G. Forman, "An Extensive Empirical Study of Feature Selection Metrics for Text Classification," J. Machine Learning Research, vol. 3, pp. 1289-1305, Mar. 2003. [19] L. Wolf and A. Shashua, "Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach," J. Machine Learning Research, vol. 6, pp. 1855-1887, 2005. [20] L. Wolf and S. Bileschi, "Combining Variable Selection with Dimensionality Reduction," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 20-25, June 2005. [21] D. Cai, X. He, Y. Hu, J. Han, and T. Huang, "Learning a Spatially Smooth Subspace for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-7, July 2007. [22] S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, Nov. 1984. [23] J.L. Marroquin, F.A. Velasco, M. Rivera, and M. Nakamura, "Gauss-Markov Measure Field Models for Low-Level Vision," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 337-348, Apr. 2001. [24] M. Zhu and T.J. Hastie, "Feature Extraction for Nonparametric Discriminant Analysis," J. Computational and Graphical Statistics, vol. 12, no. 1, pp. 101-120, Mar. 2003. [25] N. Alyuz, B. Gokberk, and L. Akarun, "Regional Registration and Curvature Descriptors for Expression Resistant 3D Face Recognition," Proc. 17th IEEE Signal Processing and Comm. Applications Conf., pp. 544-547, Apr. 2009. [26] M. Husken, M. Brauckmann, S. Gehlen, and C.V. der Malsburg, "Strategies and Benefits of Fusion of 2D and 3D Face Recognition," Proc. IEEE Workshop Face Recognition Grand Challenge Experiments, pp. 174-181, June 2005. [27] F. Al-Osaimi, M. Bennamoun, and A. Mian, "An Expression Deformation Approach to Non-Rigid 3D Face Recognition," Int'l J. Computer Vision, vol. 81, no. 3, pp. 302-316, Mar. 2009. [28] C. Queirolo, L. Silva, O. Bellon, and M. Segundo, "3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 206-219, Feb. 2010. [29] C. Boehnen, T. Peters, and P. Flynn, "3D Signatures for Fast 3D Face Recognition," Proc. Third Int'l Conf. Advances in Biometrics, pp. 12-21, June 2009. [30] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, "LIBLINEAR: A Library for Large Linear Classification," J. Machine Learning Research, vol. 9, pp. 1871-1874, Aug. 2008.