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Issue No.01 - Jan. (2014 vol.36)
pp: 113-126
Sumit Shekhar , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Vishal M. Patel , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Nasser M. Nasrabadi , U.S. Army Res. Lab., Adelphi, MD, USA
Rama Chellappa , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
ABSTRACT
Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.
INDEX TERMS
sparse representation, Multimodal biometrics, feature fusion,
CITATION
Sumit Shekhar, Vishal M. Patel, Nasser M. Nasrabadi, Rama Chellappa, "Joint Sparse Representation for Robust Multimodal Biometrics Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 1, pp. 113-126, Jan. 2014, doi:10.1109/TPAMI.2013.109
REFERENCES
[1] A. Ross, K. Nandakumar, and A.K. Jain, Handbook of Multibiometrics. Springer, 2006.
[2] A. Ross and A.K. Jain, "Multimodal Biometrics: An Overview," Proc. European Signal Processing Conf., pp. 1221-1224, Sept. 2004.
[3] P. Krishnasamy, S. Belongie, and D. Kriegman, "Wet Fingerprint Recognition: Challenges and Opportunities," Proc. Int'l Joint Conf. Biometrics, pp. 1-7, Oct. 2011.
[4] A. Klausner, A. Tengg, and B. Rinner, "Vehicle Classification on Multi-Sensor Smart Cameras Using Feature- and Decision-Fusion," Proc. IEEE Conf. Distributed Smart Cameras, pp. 67-74, Sept. 2007.
[5] A. Rattani, D. Kisku, M. Bicego, and M. Tistarelli, "Feature Level Fusion of Face and Fingerprint Biometrics," Proc. IEEE Int'l Conf. Biometrics: Theory, Applications, and Systems, pp. 1-6, Sept. 2007.
[6] X. Zhou and B. Bhanu, "Feature Fusion of Face and Gait for Human Recognition at a Distance in Video," Proc. Int'l Conf. Pattern Recognition, vol. 4, pp. 529-532, Aug. 2006.
[7] A.A. Ross and R. Govindarajan, "Feature Level Fusion of Hand and Face Biometrics," Proc. SPIE, vol. 5779, pp. 196-204, Mar. 2005.
[8] M. Gönen and E. Alpaydn, "Multiple Kernel Learning Algorithms," J. Machine Learning Research, vol. 12, pp. 2211-2268, 2011.
[9] S. Kakade and D. Foster, "Multi-View Regression via Canonical Correlation Analysis," Pro. 20th Int'l Conf. Learning Theory, pp. 82-96, 2007.
[10] V. Sindhwani and D. Rosenberg, "An RKHS for Multi-View Learning and Manifold Co-Regularization," Proc. 25th Int'l Conf. Machine learning, pp. 976-983, July 2008.
[11] J. Farquhar, H. Meng, S. Szedmak, D. Hardoon, and J. Shawe-taylor, "Two View Learning: SVM-2k, Theory and Practice," Proc. Advances in Neural Information Processing Systems, Dec. 2006.
[12] T. Diethe, D. Hardoon, and J. Shawe-Taylor, "Constructing Nonlinear Discriminants from Multiple Data Views," Machine Learning and Knowledge Discovery in Databases, pp. 328-343, 2010.
[13] S. Kim, A. Magnani, and S. Boyd, "Optimal Kernel Selection in Kernel Fisher Discriminant Analysis," Proc. 23rd Int'l Conf. Machine Learning, pp. 465-472, June 2006.
[14] V.M. Patel and R. Chellappa, "Sparse Representations, Compressive Sensing and Dictionaries for Pattern Recognition," Proc. Asian Conf. Pattern Recognition, pp. 325-329, Nov. 2011.
[15] V.M. Patel, R. Chellappa, and M. Tistarelli, "Sparse Representations and Random Projections for Robust and Cancelable Biometrics," Proc. Int'l Conf. Control, Automation, Robotics, and Vision, pp. 1-6, Dec. 2010.
[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] J.K. Pillai, V.M. Patel, R. Chellappa, and N.K. Ratha, "Secure and Robust Iris Recognition Using Random Projections and Sparse Representations," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no 9, pp. 1877-1893, Sept. 2011.
[18] P. Nagesh and B. Li, "A Compressive Sensing Approach for Expression-Invariant Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1518-1525, June 2009.
[19] V.M. Patel, T. Wu, S. Biswas, P. Phillips, and R. Chellappa, "Dictionary-Based Face Recognition under Variable Lighting and Pose," IEEE Trans. Information Forensics and Security, vol. 7, no. 3, pp. 954-965, June 2012.
[20] Q. Zhang and B. Li, "Discriminative K-SVD for Dictionary Learning in Face Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2691-2698, June 2010.
[21] J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. Huang, and S. Yan, "Sparse Representation for Computer Vision and Pattern Recognition," Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, June 2010.
[22] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, "Towards a Practical Face Recognition System: Robust Alignment and Illumination via Sparse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 372-386, Feb. 2012.
[23] E. Elhamifar and R. Vidal, "Robust Classification Using Structured Sparse Representation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1873-1879, June 2011.
[24] M. Yuan and Y. Lin, "Model Selection and Estimation in Regression with Grouped Variables," J. Royal Statistical Soc.: Series B, vol. 68, pp. 49-67, Feb. 2006.
[25] L. Meier, S.V.D. Geer, and P. Bhlmann, "The Group Lasso for Logistic Regression," J. Royal Statistical Soc.: Series B, vol. 70, pp. 53-71, Feb. 2008.
[26] H. Zhang, N.M. Nasrabadi, Y. Zhang, and T.S. Huang, "Multi-Observation Visual Recognition via Joint Dynamic Sparse Representation," Proc. IEEE Int'l Conf. Computer Vision, pp. 595-602, Nov. 2011.
[27] X.-T. Yuan and S. Yan, "Visual Classification with Multi-Task Joint Sparse Representation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 3493-3500, June 2010.
[28] B. Cheng, G. Liu, J. Wang, Z. Huang, and S. Yan, "Multi-Task Low-Rank Affinity Pursuit for Image Segmentation," Proc. IEEE Int'l Conf. Computer Vision, pp. 2439-2446, Nov. 2011.
[29] S. Shekhar, V.M. Patel, N.M. Nasrabadi, and R. Chellappa, "Joint Sparsity-Based Robust Multimodal Biometrics Recognition," Proc. ECCV Workshop Information Fusion in Computer Vision for Concept Recognition, Oct. 2012.
[30] N.H. Nguyen, N.M. Nasrabadi, and T.D. Tran, "Robust Multi-Sensor Classification via Joint Sparse Representation," Proc. Int'l Conf. Information Fusion, pp. 1-8, July 2011.
[31] R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," J. Royal Statistical Soc.: Series B, vol. 58, pp. 267-288, 1996.
[32] E.J. Candes, X. Li, Y. Ma, and J. Wright, "Robust Principal Component Analysis?" J. ACM, vol. 58, pp. 1-37, May 2011.
[33] J. Yang and Y. Zhang, "Alternating Direction Algorithms for l1 Problems in Compressive Sensing," SIAM J. Scientific Computing, vol. 33, pp. 250-278, 2011.
[34] M. Afonso, J. Bioucas-Dias, and M. Figueiredo, "An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems," IEEE Trans. Image Processing, vol. 20, no. 3, pp. 681-695, Mar. 2011.
[35] S.S.S. Crihalmeanu, A. Ross, and L. Hornak, "A Protocol for Multibiometric Data Acquisition, Storage and Dissemination," technical report, Lane Dept. of Computer Science and Electrical Eng., West Virginia Univ., 2007.
[36] A. Martnez and R. Benavente, "The AR Face Database," CVC technical report, June 1998.
[37] U. Park, R. Jillela, A. Ross, and A. Jain, "Periocular Biometrics in the Visible Spectrum," IEEE Trans. Information Forensics and Security, vol. 6, no. 1, pp. 96-106, Mar. 2011.
[38] A. Moorhouse, A. Evans, G. Atkinson, J. Sun, and M. Smith, "The Nose on Your Face May Not Be So Plain: Using the Nose as a Biometric," Proc. Int'l Conf. Crime Detection and Prevention, pp. 1-6, Dec. 2009.
[39] P. Sinha, B. Balas, Y. Ostrovsky, and R. Russell, "Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About," Proc. IEEE, vol. 94, no. 11, pp. 1948-1962, Nov. 2006.
[40] H. Li, K.-A. Toh, and L. Li, Advanced Topics In Biometrics. World Scientific Publishing Co. Pvt. Ltd., 2012.
[41] S. Pundlik, D. Woodard, and S. Birchfield, "Non-Ideal Iris Segmentation Using Graph Cuts," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, pp. 1-6, June 2008.
[42] L. Masek and P. Kovesi, "MATLAB Source Code for Biometric Identification System Based on Iris Patterns," technical report, The Univ. of Western Australia, 2003.
[43] C.W.S. Chikkerur and V. Govindaraju, "A Systematic Approach for Feature Extraction in Fingerprint Images," Proc. Int'l Conf. Bioinformatics and Its Applications, pp. 344-350, Dec. 2004.
[44] A. Jain, S. Prabhakar, L. Hong, and S. Pankanti, "Filterbank-Based Fingerprint Matching," IEEE Trans. Image Processing, vol. 9, no. 5, pp. 846-859, May 2000.
[45] J. Daugman, "How Iris Recognition Works," IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, Jan. 2004.
[46] B. Krishnapuram, L. Carin, M. Figueiredo, and A. Hartemink, "Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 957-968, June 2005.
[47] C.J. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, June 1998.
[48] A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet, "SimpleMKL," J. Machine Learning Research, vol. 9, pp. 2491-2521, 2008.
[49] R. Bolle, J. Connell, S. Pankanti, N. Ratha, and A. Senior, "The Relation between the ROC Curve and the CMC," Proc. Fourth IEEE Workshop Automatic Identification Advanced Technologies, pp. 15-20, 2005.
[50] K. Nandakumar, Y. Chen, S. Dass, and A. Jain, "Likelihood Ratio-Based Biometric Score Fusion," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 342-347, Feb. 2008.
[51] X.F.M. Yang, L. Zhang, and D. Zhang, "Fisher Discrimination Dictionary Learning for Sparse Representation," Proc. IEEE Int'l Conf. Computer Vision, pp. 543-550, Nov. 2011.
[52] A.M. Bruckstein, D.L. Donoho, and M. Elad, "From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images," SIAM Rev., vol. 51, no. 1, pp. 34-81, Feb. 2009.
[53] M. Elad, Sparse and Redundant Representations. Springer, 2010.
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