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Issue No.02 - Feb. (2013 vol.35)
pp: 381-397
H. Mohammadzade , Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
D. Hatzinakos , Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
ABSTRACT
The common approach for 3D face recognition is to register a probe face to each of the gallery faces and then calculate the sum of the distances between their points. This approach is computationally expensive and sensitive to facial expression variation. In this paper, we introduce the iterative closest normal point method for finding the corresponding points between a generic reference face and every input face. The proposed correspondence finding method samples a set of points for each face, denoted as the closest normal points. These points are effectively aligned across all faces, enabling effective application of discriminant analysis methods for 3D face recognition. As a result, the expression variation problem is addressed by minimizing the within-class variability of the face samples while maximizing the between-class variability. As an important conclusion, we show that the surface normal vectors of the face at the sampled points contain more discriminatory information than the coordinates of the points. We have performed comprehensive experiments on the Face Recognition Grand Challenge database, which is presently the largest available 3D face database. We have achieved verification rates of 99.6 and 99.2 percent at a false acceptance rate of 0.1 percent for the all versus all and ROC III experiments, respectively, which, to the best of our knowledge, have seven and four times less error rates, respectively, compared to the best existing methods on this database.
INDEX TERMS
Face, Three dimensional displays, Nose, Face recognition, Databases, Vectors, Principal component analysis,LDA, Three-dimensional, face recognition, expression variation, point correspondence, 3D registration, surface normal vector
CITATION
H. Mohammadzade, D. Hatzinakos, "Iterative Closest Normal Point for 3D Face Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 2, pp. 381-397, Feb. 2013, doi:10.1109/TPAMI.2012.107
REFERENCES
[1] K.I. Chang, K.W. Bowyer, and P.J. Flynn, "Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1695-1700, Oct. 2006.
[2] A. Mian, M. Bennamoun, and R. Owens, "An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 11, pp. 1927-1943, Nov. 2007.
[3] T. Maurer, D. Guigonis, I. Maslov, B. Pesenti, A. Tsaregorodtsev, D. West, and G. Medioni, "Performance of Geometrix Activeid 3D Face Recognition Engine on the FRGC Data," Proc. IEEE Conf. Computer Vision and Pattern Recognition, p. 154, 2005.
[4] M. Husken, M. Brauckmann, S. Gehlen, and C.V. der Malsburg, "Strategies and Benefits of Fusion of 2D and 3D Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, p. 174, 2005.
[5] W.-Y. Lin, K.-C. Wong, N. Boston, and Y.H. Hu, "3D Face Recognition under Expression Variations Using Similarity Metrics Fusion," Proc. IEEE Int'l Conf. Multimedia and Expo, pp. 727-730, 2007.
[6] J. Cook, C. McCool, V. Chandran, and S. Sridharan, "Combined 2D/3D Face Recognition Using Log-Gabor Templates," Proc. IEEE Int'l Conf. Video and Signal Based Surveillance, p. 83, 2006.
[7] C.C. Queirolo, L. Silva, O.R.P. Bellon, and M.P. 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.
[8] P.J. Besl and N.D. McKay, "A Method for Registeration of 3-D Shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
[9] I. Kakadiaris, G. Passalis, G. Toderici, N. Murtuza, and T. Theoharis, "Three-Dimensional Face Recognition in the Presence of Facial Expression: An Annotated Deformable Model Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 640-649, Apr. 2007.
[10] 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, pp. 302-316, 2009.
[11] J. Huang, B. Heisele, and V. Blanz, "Component-Based Face Recognition with 3D Morphable Models," Proc. Int'l Conf. Audio and Video-Based Biometric Person Authentication, pp. 27-34, 2003.
[12] X. Lu and A.K. Jain, "Deformation Modeling for Robust 3D Face Matching," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1377-1383, 2006.
[13] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[14] G. Baudat and F. Anouar, "Generalized Discriminant Analysis Using a Kernel Approach," Neural Computation, vol. 12, pp. 2385-2404, 2000.
[15] J. Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms," IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 117-126, Jan. 2003.
[16] T. Hastie, A. Buja, and R. Tibshirani, "Penalized Discriminant Analysis," Annals of Statistics, vol. 23, pp. 73-102, 1995.
[17] M. Loog and R.P.W. Duin, "Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 732-739, June 2004.
[18] M. Zhu and A.M. Martinez, "Subclass Discriminant Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1274-1286, Aug. 2006.
[19] H. Yu and J. Yang, "A Direct LDA Algorithm for High-Dimensional Data with Applications to Face Recognition," Pattern Recognition, vol. 34, pp. 2067-2070, 2001.
[20] J. Wang, K.N. Plataniotis, J. Lu, and A.N. Venetsanopoulosc, "Kernel Quadratic Discriminant Analysis for Small Sample Size Problem," Pattern Recognition, vol. 41, pp. 1528-1538, 2008.
[21] H. Mohammadzade and D. Hatzinakos, "An Expression Transformation for Improving the Recognition of Expression-Variant Faces from One Sample Image Per Person," Proc. IEEE Int'l Conf. Biometrics: Theory Applications and Systems, pp. 1-6, 2010.
[22] T. Heseltine, N. Pears, and J. Austin, "Three-Dimensional Face Recognition: An Eigensurface Approach," Proc. Int'l Conf. Image Processing, pp. 1421-1424, 2004.
[23] C. Hesher, A. Srivastava, and G. Erlebacher, "A Novel Technique for Face Recognition Using Range Imaging," Proc. Int'l Symp. Signal Processing and Its Applications, vol. 2, pp. 201-204, 2003.
[24] X. Yuan, J. Lu, and T. Yahagi, "A Method of 3D Face Recognition Based on Principal Component Analysis Algorithm," Proc. IEEE Symp. Circuits and Systems, vol. 4, pp. 3211-3214, 2005.
[25] T. Russ, C. Boehnen, and T. Peters, "3D Face Recognition Using 3D Alignment for PCA," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[26] 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.
[27] V. Blanz, K. Scherbaum, and H.P. Seidel, "Fitting a Morphable Model to 3D Scans of Faces," Proc. 11th IEEE Int'l Conf. Computer Vision, pp. 1-8, 2007.
[28] Y. Chen and G. Medioni, "Object Modelling by Registration of Multiple Range Images," Image and Vision Computing, vol. 10, pp. 145-155, 1992.
[29] 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 Conf. Computer Vision and Pattern Recognition, pp. 947-954, 2005.
[30] K.W. Bowyer, K. Chang, and P.J. Flynn, "A Survey of Approaches and Challenges in 3D and Multi-Modal 3D+2D Face Recognition," Computer Vision and Image Understanding, vol. 101, pp. 1-15, 2006.
[31] D. Smeets, P. Claes, D. Vandermeulen, and J.G. Clement, "Objective 3D Face Recognition: Evolution, Approaches and Challenges," Forensic Science Int'l, vol. 201, pp. 125-132, 2010.
[32] O. Ocegueda, S.K. Shah, and I.A. Kakadiaris, "Which Parts of the Face Give Out Your Identity?" Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 641-648, 2011.
[33] T. Faltemier, K.W. Bowyer, and P.J. Flynn, "A Region Ensemble for 3D Face Recognition," IEEE Trans. Information Forensics and Security, vol. 3, no. 1, pp. 62-73, Mar. 2008.
[34] R. McKeon, "Three-Dimensional Face Imaging and Recognition: A Sensor Design and Comparative Study," PhD dissertation, Univ. of Notre Dame, 2010.
[35] L. Spreeuwers, "Fast and Accurate 3D Face Recognition," Int'l J. Computer Vision, vol. 93, pp. 389-414, 2011.
[36] D. Huang, M. Ardabilian, Y. Wang, and L. Chen, "A Novel Geometric Facial Representation Based on Multi-Scale Extended Local Binary Patterns," Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 1-7, 2011.
[37] A. Pentland, B. Moghaddam, and T. Starner, "View-Based and Modular Eigenspaces for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 19, pp. 696-710, 1994.
[38] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 37, no. 1, pp. 2-86, 1991.
[39] M.P. Segundo, C. Queirolo, O.R.P. Bellon, and L. Silva, "Automatic 3D Facial Segmentation and Landmark Detection," Proc. Int'l Conf. Image Analysis and Processing, pp. 431-436, 2007.
[40] J.S. Lim, Two-Dimensional Signal and Image Processing. Prentice Hall, 1990.
[41] K. Arun, T. Huang, and S. Blostein, "Least-Squares Fitting of Two 3-D Point Sets," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 5, pp. 698-700, Sept. 1987.
[42] A.M. Bronstein, M.M. Bronstein, and R. Kimmel, "Expression-Invariant Representations of Faces," IEEE Trans. Image Processing, vol. 16, no. 1, pp. 188-197, Jan. 2007.
[43] J. Wang, K.N. Plataniotis, J. Lu, and A.N. Venetsanopoulos, "On Solving the Face Recognition Problem with One Training Sample per Subject," Pattern Recognition, vol. 39, pp. 1746-1762, 2006.
[44] D.H. Wolpert, "Stacked Generalization," Neural Networks, vol. 5, no. 2, pp. 241-259, 1992.
[45] J. Kittler, M. Hatef, R. Duin, and J. Matas, "On Combining Classifiers," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[46] K. Tumer and J. Ghosh, "Robust Combining of Disparate Classifiers through Order Statistics," Pattern Analysis and Applications, vol. 5, no. 2, pp. 189-200, 2002.
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