The Community for Technology Leaders
RSS Icon
Issue No.01 - Jan.-March (2013 vol.4)
pp: 69-82
Hoda Mohammadzade , University of Toronto, Toronto
Dimitrios Hatzinakos , University of Toronto, Toronto
Discriminant analysis methods are powerful tools for face recognition. However, these methods cannot be used for the single sample per person scenario because the within-subject variability cannot be estimated in this case. In the generic learning solution, this variability is estimated using images of a generic training set, for which more than one sample per person is available. However, because of rather poor estimation of the within-subject variability using a generic set, the performance of discriminant analysis methods is yet to be satisfactory. This problem particularly exists when images are under drastic facial expression variation. In this paper, we show that images with the same expression are located on a common subspace, which here we call it the expression subspace. We show that by projecting an image with an arbitrary expression into the expression subspaces, we can synthesize new expression images. By means of the synthesized images for subjects with one image sample, we can obtain more accurate estimation of the within-subject variability and achieve significant improvement in recognition. We performed comprehensive experiments on two large face databases: the Face Recognition Grand Challenge and the Cohn-Kanade AU-Coded Facial Expression database to support the proposed methodology.
Face recognition, Training, Databases, Eigenvalues and eigenfunctions, generic training, Face recognition, Training, Databases, Eigenvalues and eigenfunctions, single sample per person, Face recognition, facial expression, expression variation, expression transformation, expression subspace, LDA
Hoda Mohammadzade, Dimitrios Hatzinakos, "Projection into Expression Subspaces for Face Recognition from Single Sample per Person", IEEE Transactions on Affective Computing, vol.4, no. 1, pp. 69-82, Jan.-March 2013, doi:10.1109/T-AFFC.2012.30
[1] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, pp. 399-459, 2003.
[2] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, "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.
[3] 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.
[4] 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.
[5] T. Hastie, A. Buja, and R. Tibshirani, "Penalized Discriminant Analysis," Ann. of Statistics, vol. 23, pp. 73-102, 1995.
[6] G. Baudat and F. Anouar, "Generalized Discriminant Analysis Using a Kernel Approach," Neural Computation, vol. 12, pp. 2385-2404, 2000.
[7] 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.
[8] M. Zhu and A.M. Martinez, "Subclass Discriminant Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1274-1286, 2006.
[9] Y. Liu, K. Schmidt, J. Cohn, and S. Mitra, "Facial Asymmetry Quantification for Expression Invariant Human Identification," Computer Vision and Image Understanding, vol. 91, pp. 138-159, 2003.
[10] 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.
[11] H. Mohammadzade and D. Hatzinakos, "Correspondence Normal Difference: An Aligned Representation of 3D Faces to Apply Discriminant Analysis Methods," Proc. Int'l Conf. Digital Signal Processing, pp. 1-8, 2011.
[12] X. Tan, S. Chen, Z. Zhou, and F. Zhang, "Face Recognition from a Single Image per Person: A Survey," Pattern Recognition, vol. 39, pp. 1725-1745, 2006.
[13] 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.
[14] X. Li, G. Mori, and H. Zhang, "Expression-Invariant Face Recognition with Expression Classification," Proc. Third Canadian Conf. Computer and Robot Vision, p. 77, 2006.
[15] H.S. Lee and D. Kim, "Expression-Invariant Face Recognition by Facial Expression Transformations," Pattern Recognition Letters, vol. 29, pp. 1797-1805, 2008.
[16] M. Vasilescu and D. Terzopoulos, "Multilinear Analysis of Image Ensembles: Tensorfaces," Proc. European Conf. Computer Vision, pp. 447-460, 2002.
[17] 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.
[18] 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.
[19] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 37, no. 1, pp. 2-86, 1991.
[20] T. Kanade, J.F. Cohn, and Y.L. Tian, "Comprehensive Database for Facial Expression Analysis," Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, 1999.
[21] Y. Su, S. Shan, X. Chen, and W. Gao, "Adaptive Generic Learning for Face Recognition from a Single Sample per Person," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2699-2706, 2010.
[22] 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.
[23] A. Papoulis and S.U. Pillai, Probability, Random Variables and Stochastic Processes, fourth ed. McGraw-Hill, 2002.
32 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool