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Issue No.10 - October (2011 vol.33)
pp: 1952-1961
Jingu Heo , Carnegie Mellon University, Pittsburgh
Utsav Prabhu , Carnegie Mellon University, Pittsburgh
ABSTRACT
Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.
INDEX TERMS
Pose-invariant face recognition, generic elastic models, 3D face modeling.
CITATION
Jingu Heo, Utsav Prabhu, "Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 10, pp. 1952-1961, October 2011, doi:10.1109/TPAMI.2011.123
REFERENCES
[1] M.A.O. Vasilescu and D. Terzopoulos, "Multilinear Analysis of Image Ensembles: TensorFaces," Proc. European Conf. Computer Vision, vol. 1, pp. 447-460, 2002.
[2] S.W. Park and M. Savvides, "Individual Kernel Tensor-Subspaces for Robust Face Recognition: A Computationally Efficient Tensor Framework without Requiring Mode Factorization," IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 5, pp. 1156-1166, Oct. 2007.
[3] A. Pentland, B. Moghaddam, and T. Starner, "View-Based and Modular Eigenspaces for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 84-91, 1994.
[4] V. Blanz and T. Vetter, "Face Recognition Based on Fitting a 3D Morphable Model," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1063-1074, Sept. 2003.
[5] B.K. Horn, "Shape from Shading: A Method for Obtaining the Shape of a Smooth Opaque Object from One View," technical report, Massachusetts Inst. of Tech nology 1970.
[6] R. Zhang, P.-S. Tsai, J.E. Cryer, and M. Shah, "Shape from Shading: A Survey," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 8, pp. 690-706, Aug. 1999.
[7] J. Xiao, J. Chai, and T. Kanade, "A Closed-Form Solution to Non-Rigid Shape and Motion Recovery," Int'l J. Computer Vision, vol. 67, no. 2, pp. 233-246, 2006.
[8] J. Heo, "Generic Elastic Models for 2D Pose Synthesis and Face Recognition," PhD dissertation, Carnegie Mellon Univ., 2009.
[9] C. Xie, M. Savvides, and B.V.K.V. Kumar, "Kernel Correlation Filter Based Redundant Class-Dependence Feature Analysis (KCFA) on FRGC2.0 Data," Proc. IEEE Second Int'l Conf. Analysis and Modelling of Faces and Gestures, vol. 3723, pp. 32-43, 2005.
[10] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[11] T.F. Cootes, C.J. Taylor, D.H. Cooper, and J. Graham, "Active Shape Models—Their Training and Application," Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, 1995.
[12] G.J. Edwards, T.F. Cootes, and C.J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, pp. 681-685, June 2001.
[13] I. Matthews and S. Baker, "Active Appearance Models Revisited," Int'l J. Computer Vision, vol. 60, pp. 135-164, 2003.
[14] P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, "Overview of the Face Recognition Grand Challenge," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 947-954, 2005.
[15] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, pp. 399-458, Dec. 2003.
[16] J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, "Robust Face Recognition via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
[17] X. Chai, S. Shan, and W. Gao, "Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation," Proc. IEEE Fourth Pacific-Rim Conf. Multimedia, vol. 2, pp. 1413-1417, 2003.
[18] G. Shakhnarovich and B. Moghaddam, "Face Recognition in Subspaces," Handbook of Face Recognition, pp. 141-168, Springer, 2004.
[19] R. Brunelli and T. Poggio, "Face Recognition: Features Versus Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052, Oct. 1993.
[20] L. Wiskott, J. Fellous, N. Krger, 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.
[21] S.K. Zhou and R. Chellappa, "From Sample Similarity to Ensemble Similarity: Probabilistic Distance Measures in Reproducing Kernel Hilbert Space," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 917-929, June 2006.
[22] W.Y. Zhao and R. Chellappa, "SFS Based View Synthesis for Robust Face Recognition," Proc. IEEE Fourth Int'l Conf. Automatic Face and Gesture Recognition, pp. 285-292, 2000.
[23] K. Seshadri and M. Savvides, "Robust Modified Active Shape Model for Automatic Facial Landmark Annotation of Frontal Faces," Proc. IEEE Third Int'l Conf. Biometrics: Theory, Applications and Systems, pp. 319-326, 2009.
[24] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, "Multi-PIE," Proc. IEEE Eighth Int'l Conf. Automatic Face and Gesture Recognition, pp. 1-8, 2008.
[25] T. Ojala, M. Pietikainen, and D. Harwood, "Performance Evaluation of Texture Measures with Classification Based on Kullback Discrimination of Distributions," Proc. 12th Int'l Conf. Pattern Recognition, vol. 1, pp. 582-585, Oct. 1994.
[26] D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental Learning for Robust Visual Tracking," Int'l J. Computer Vision, vol. 77, no. 1, pp. 125-141, May 2008.
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