The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.12 - Dec. (2013 vol.35)
pp: 3037-3049
Soma Biswas , Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Gaurav Aggarwal , Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Patrick J. Flynn , Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Kevin W. Bowyer , Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
ABSTRACT
Face images captured by surveillance cameras usually have poor resolution in addition to uncontrolled poses and illumination conditions, all of which adversely affect the performance of face matching algorithms. In this paper, we develop a completely automatic, novel approach for matching surveillance quality facial images to high-resolution images in frontal pose, which are often available during enrollment. The proposed approach uses multidimensional scaling to simultaneously transform the features from the poor quality probe images and the high-quality gallery images in such a manner that the distances between them approximate the distances had the probe images been captured in the same conditions as the gallery images. Tensor analysis is used for facial landmark localization in the low-resolution uncontrolled probe images for computing the features. Thorough evaluation on the Multi-PIE dataset and comparisons with state-of-the-art super-resolution and classifier-based approaches are performed to illustrate the usefulness of the proposed approach. Experiments on surveillance imagery further signify the applicability of the framework. We also show the usefulness of the proposed approach for the application of tracking and recognition in surveillance videos.
INDEX TERMS
Facial recognition, Iterative methods, Resolution, Cameras, Surveillance,iterative majorization, Face recognition, low-resolution matching, multidimensional scaling
CITATION
Soma Biswas, Gaurav Aggarwal, Patrick J. Flynn, Kevin W. Bowyer, "Pose-Robust Recognition of Low-Resolution Face Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 12, pp. 3037-3049, Dec. 2013, doi:10.1109/TPAMI.2013.68
REFERENCES
[1] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, "Guide to the CMU Multi-Pie Database," technical report, Carnegie Mellon Univ., 2007.
[2] 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.
[3] S. Romdhani, V. Blanz, and T. Vetter, "Face Identification by Fitting a 3D Morphable Model Using Linear Shape and Texture Error Functions," Proc. European Conf. Computer Vision, pp. 3-19, 2002.
[4] L. Zhang and D. Samaras, "Face Recognition from a Single Training Image under Arbitrary Unknown Lighting Using Spherical Harmonics," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 351-363, Mar. 2006.
[5] S. Prince, J. Warrell, J. Elder, and F. Felisberti, "Tied Factor Analysis for Face Recognition across Large Pose Differences," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 6, pp. 970-984, June 2008.
[6] S. Baker and T. Kanade, "Hallucinating Faces," Proc. Fourth IEEE Int'l Conf. Automatic Face and Gesture Recognition, Mar. 2000.
[7] S. Baker and T. Kanade, "Limits on Super-Resolution and How to Break Them," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1167-1183, Sept. 2002.
[8] P. Hennings-Yeomans, S. Baker, and B. Kumar, "Simultaneous Super-Resolution and Feature Extraction for Recognition of Low-Resolution Faces," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[9] P. Hennings-Yeomans, B. Kumar, and S. Baker, "Robust Low-Resolution Face Identification and Verification Using High-Resolution Features," Proc. Int'l Conf. Image Processing, pp. 33-36, 2009.
[10] A. Chakrabarti, A. Rajagopalan, and R. Chellappa, "Super-Resolution of Face Images Using Kernel PCA-Based Prior," IEEE Trans. Multimedia, vol. 9, no. 4, pp. 888-892, June 2007.
[11] C. Liu, H.Y. Shum, and W.T. Freeman, "Face Hallucination: Theory and Practice," Int'l J. Computer Vision, vol. 75, no. 1, pp. 115-134, 2007.
[12] T. Marciniak, A. Dabrowski, A. Chmielewska, and R. Weychan, "Face Recognition from Low Resolution Images," Proc. Int'l Conf. Multimedia Comm., Services, and Security, pp. 220-229, 2012.
[13] W. Hwang, X. Huang, K. Noh, and J. Kim, "Face Recognition System Using Extended Curvature Gabor Classifier Bunch for Low-Resolution Face Image," Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, pp. 15-22, 2011.
[14] I. Borg and P. Groenen, Modern Multidimensional Scaling: Theory and Applications, second ed. Springer, 2005.
[15] J. Yang, J. Wright, T. Huang, and Y. Ma, "Image Super-Resolution via Sparse Representation," IEEE Trans. Image Processing, vol. 19, no. 11, pp. 2861-2873, Nov. 2010.
[16] K.Q. Weinberger and L.K. Saul, "Fast Solvers and Efficient Implementations for Distance Metric Learning," Proc. Int'l Conf. Machine Learning, vol. 307, pp. 1160-1167, 2008.
[17] M. Grgic, K. Delac, and S. Grgic, "SCface—Surveillance Cameras Face Database," Multimedia Tools and Applications J., vol. 51, pp. 863-879, 2009.
[18] S. Biswas, G. Aggarwal, and P. Flynn, "Pose-Robust Recognition of Low-Resolution Face Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
[19] S. Biswas, G. Aggarwal, and P.J. Flynn, "Face Recognition in Low-Resolution Videos Using Learning-Based Likelihood Measurement Model," Proc. Int'l Joint Conf. Biometrics, 2011.
[20] T. Kanade and A. Yamada, "Multi-Subregion Based Probabilistic Approach toward Pose-Invariant Face Recognition," Proc. IEEE Int'l Symp. Computational Intelligence in Robotics and Automation, pp. 954-959, 2003.
[21] H. Chang, D. Yeung, and Y. Xiong, "Super-Resolution through Neighbor Embedding," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 275-282, 2004.
[22] W. Liu, D. Lin, and X. Tang, "Hallucinating Faces: Tensorpatch Super-Resolution and Coupled Residue Compensation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 478-484, 2005,
[23] B. Gunturk, A. Batur, Y. Altunbasak, M. Hayes, and R. Mersereau, "Eigenface-Domain Super-Resolution for Face Recognition," IEEE Trans. Image Processing, vol. 12, no. 5, pp. 597-606, May 2003.
[24] O. Arandjelovic and R. Cipolla, "A Manifold Approach to Face Recognition from Low Quality Video across Illumination and Pose Using Implicit Super-Resolution," Proc. IEEE Int'l Conf. Computer Vision, 2007.
[25] S. Biswas, K.W. Bowyer, and P.J. Flynn, "Multidimensional Scaling for Matching Low-Resolution Facial Images," Proc. IEEE Int'l Conf. Biometrics: Theory, Applications, and Systems, 2010.
[26] K. Jia and S. Gong, "Multi-Modal Tensor Face for Simultaneous Super-Resolution and Recognition," Proc. IEEE Int'l Conf. Computer Vision, pp. 1683-1690, 2005.
[27] M. Nishiyama, H. Takeshima, J. Shotton, T. Kozakaya, and O. Yamaguchi, "Facial Deblur Inference to Improve Recognition of Blurred Faces," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1115-1122, 2009.
[28] M. Guillaumin, J. Verbeek, and C. Schmid, "Is That You? Metric Learning Approaches for Face Identification," Proc. IEEE Int'l Conf. Computer Vision, 2009.
[29] P. Dreuw, P. Steingrube, H. Hanselmann, and N. Hermann, "Surf-Face: Face Recognition under Viewpoint Consistency Constraints," Proc. British Machine Vision Conf., 2009.
[30] A. Webb, "Multidimensional Scaling by Iterative Majorization Using Radial Basis Functions," Pattern Recognition, vol. 28, no. 5, pp. 753-759, May 1995.
[31] M.A.O. Vasilescu and D. Terzopoulos, "Multilinear Analysis of Image Ensembles: Tensorfaces," Proc. European Conf. Computer Vision, pp. 447-460, 2002.
[32] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neurosicence, vol. 3, no. 1, pp. 71-86, 1991.
[33] S. Milborrow and F. Nicolls, "Locating Facial Features with an Extended Active Shape Model," Proc. European Conf. Computer Vision, http://www.milbo.users.sonic.netstasm, 2008.
[34] P.J. Phillips, P.J. Flynn, J.R. Beveridge, W.T. Scruggs, A.J. O'Toole, D.S. Bolme, K.W. Bowyer, A. Draper Bruce, G.H. Givens, Y.M. Lui, H. Sahibzada, J.A. Scallan, and S. Weimer, "Overview of the Multiple Biometrics Grand Challenge," Proc. Int'l Conf. Biometrics, pp. 705-714, 2009.
[35] P. Phillips, P. Flynn, T. Scruggs, K. 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.
[36] I.K. Kim and Y. Kwon, "Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, June 2010.
[37] http://www.ifp.illinois.edu~jyang29/, 2013.
[38] http://www.mpi-inf.mpg.de~kkim/, 2013.
[39] http://www.cse.wustl.edu/~kilian/codecode.html , 2013.
[40] T. Ahonen, A. Hadid, and M. Pietikinen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, Dec. 2006.
[41] 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.
[42] S.K. Zhao, V. Krueger, and R. Chellappa, "Probabilistic Recognition of Human Faces from Video," Computer Vision and Image Understanding, vol. 91, pp. 214-245, 2003.
[43] B. Moghaddam, "Principal Manifolds and Probabilistic Subspaces for Visual Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 780-788, June 2002.
[44] https://sites.google.com/site/skevinzhou codes/, 2013.
53 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool