The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.11 - November (2010 vol.32)
pp: 2106-2112
Imran Naseem , The University of Western Australia, Crawley
Roberto Togneri , The University of Western Australia, Crawley
Mohammed Bennamoun , The University of Western Australia, Crawley
ABSTRACT
In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.
INDEX TERMS
Face recognition, linear regression, nearest subspace classification.
CITATION
Imran Naseem, Roberto Togneri, Mohammed Bennamoun, "Linear Regression for Face Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 11, pp. 2106-2112, November 2010, doi:10.1109/TPAMI.2010.128
REFERENCES
[1] I.T. Jolliffe, Principal Component Analysis. Springer, 1986.
[2] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[3] 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.
[4] P. Comon, "Independent Component Analysis—A New Concept?" Signal Processing, vol. 36, pp. 287-314, 1994.
[5] M. Bartlett, H. Lades, and T. Sejnowski, "Independent Component Representations for Face Recognition," Proc. SPIE Conf. Human Vision and Electronic Imaging III, pp. 528-539, 1998.
[6] A. Leonardis and H. Bischof, "Robust Recognition Using Eigenimages," Computer Vision and Image Understanding, vol. 78, no. 1, pp. 99-118, 2000.
[7] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. John Wiley & Sons, 2000.
[8] 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.
[9] R. Barsi and D. Jacobs, "Lambertian Reflection and Linear Subspaces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 2, pp. 218-233, Feb. 2003.
[10] X. Chai, S. Shan, X. Chen, and W. Gao, "Locally Linear Regression for Pose-Invariant Face Recognition," IEEE Trans. Image Processing, vol. 16, no. 7, pp. 1716-1725, July 2007.
[11] J. Chien and C. Wu, "Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1644-1649, Dec. 2002.
[12] A. Pentland, B. Moghaddam, and T. Starner, "View-Based and Modular Eigenspaces for Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1994.
[13] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer, 2001.
[14] G.A.F. Seber, Linear Regression Analysis. Wiley-Interscience, 2003.
[15] T.P. Ryan, Modern Regression Methods. Wiley-Interscience, 1997.
[16] R.G. Staudte and S.J. Sheather, Robust Estimation and Testing. Wiley-Interscience, 1990.
[17] S. Fidler, D. Skocaj, and A. Leonardis, "Combining Reconstructive and Discriminative Subspace Methods for Robust Classification and Regression by Subsampling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 337-350, Mar. 2006.
[18] F. Samaria and A. Harter, "Parameterisation of a Stochastic Model for Human Face Identification," Proc. Second IEEE Workshop Applications of Computer Vision, Dec. 1994.
[19] "Georgia Tech Face Database," http://www.anefian.comface_reco.htm, 2007.
[20] P.J. Phillips, H. Wechsler, J.S. Huang, and P.J. Rauss, "The FERET Database and Evaluation Procedure for Face-Recognition Algorithms," Image and Vision Computing, vol. 16, no. 5, pp. 295-306, 1998.
[21] A. Georghiades, P. Belhumeur, and D. 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.
[22] K.C. Lee, J. Ho, and D. Kriegman, "Acquiring Linear Subspaces for Face Recognition under Variable Lighting," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 684-698, May 2005.
[23] A. Martinez and R. Benavente, "The AR Face Database," CVC Technical Report 24, June 1998.
[24] J. Yang, D. Zhang, A.F. Frangi, and J. Yang, "Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, Jan. 2004.
[25] M.H. Yang, "Kernel Eignefaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods," Proc. Fifth IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 215-220, May 2002.
[26] P.C. Yuen and J.H. Lai, "Face Representation Using Independent Component Analysis," Pattern Recognition, vol. 35, no. 6, pp. 1247-1257, 2002.
[27] X. Jiang, B. Mandal, and A. Kot, "Eigenfeature Regularization and Extraction in Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 383-394, Mar. 2008.
[28] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, and S.Z. Li, "Ensemble-Based Discriminant Learning with Boosting for Face Recognition," IEEE Trans. Neural Networks, vol. 17, no. 1, pp. 166-178, Jan. 2006.
[29] Handbook of Face Recognition, S.Z. Li, and A.K. Jain, eds. Springer, 2005.
[30] L. Zhang and G.W. Cottrell, "When Holistic Processing Is Not Enough: Local Features Save the Day," Proc. 26th Ann. Cognitive Science Soc. Conf., 2004.
[31] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, "On Combining Classifiers," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
22 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool