Illumination-Invariant Face Recognition in Hyperspectral Images

Han Wang^1, and Glenn Healey¹

¹Computer Vision Laboratory, Department of Electrical Engineering and Computer Science, University of California, Irvine, USA

Cite this paper:
Han Wang and Glenn Healey. Illumination-Invariant Face Recognition in Hyperspectral Images. Journal of Computer Sciences and Applications. 2019; 7(1):21-30. doi: 10.12691/jcsa-7-1-4

Abstract

Illumination-invariant face recognition remains a challenging problem. Previous studies use either spatial or spectral information to address this problem. In this paper, we propose an algorithm that uses spatial and spectral information simultaneously. We first learn a basis in the spectral domain. We then extract spatial features using 2D Gabor filters. Finally, we use the basis and the spatial features to classify face images. We demonstrate the effectiveness of the algorithm on a database of 200 subjects.

Keywords:
Gabor filters hyperspectral illumination-invariant face recognition

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	W. Zhao, R. Chellappa, P. J. Phillips, “A. Rosenfeld, Face recognition: A literature survey,” ACM Computing Survey, 35 (4) (2003) 399-458.
[2]	X. Zou, J. Kittler, K. Messer, “Illumination invariant face recognition: A survey,” in: International Conference on Biometrics: Theory, Applications, and Systems, 2007, pp. 1-8.
[3]	P. Belhumeur, J. Hespanha, D. Kriegman, “Eigenfaces vs fisherfaces: Recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (7) (1997) 711-720.
[4]	P. Belhumeur, D. Kriegman, “What is the set of images of an object under all possible illumination conditions,” International Journal of Computer Vision 28 (3) (1998) 245-260.
[5]	A. Shashua, T. Riklin-Raviv, “The quotient image: class-based rerendering and recognition with varying illuminations,” IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2) (2001) 129-139.
[6]	D. J. Jobson, Z. Rahman, G. A. Woodel, “Properties and performance of a center/surround retinex,” IEEE Transactions on Image Processing: special issue on color processing 6 (3) (1997) 451-462.
[7]	H. Wang, S. Li, Y. Wang, “Face recognition under varying lighting condition using self quotient image,” in: Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, 2004, pp. 819-824.
[8]	L. Qing, S. Shan, X. Chen, W. Gao, “Face recognition under varying lighting based on the probabilistic model of gabor phase,” in: Proceedings of IEEE Conference on Pattern Recognition, 2006, pp. 1139-1142.
[9]	M. Savvides, B. V. K. V. Kumar, P. K. Khosla, “Eigenphases vs. eigenfaces,” in: Proceedings of IEEE Conference on Pattern Recognition, Vol. 3, 2004, pp. 810-813.
[10]	J. Zhang, X. Xie, “A study on the effective approach to illumination-invariant face recognition based on a single image,” Biometric Recognition (2012) 33-41.
[11]	H. Kaur, A. Kaur, “Illumination invariant face recognition,” International Journal of Computer Applications 64 (21) (2013) 23-27.
[12]	C. Fan, S. Wang, H. Zhang, “Efficient Gabor phase based illumination invariant for face recognition,” Advances in Multimedia, Vol. 2017, Article ID 1356385.
[13]	J. Zhu, , “Illumination invariant single face image recognition under heterogeneous lighting condition,” Pattern Recognition, Vol. 66, 2017, pp. 313-327.
[14]	A. Essa, Asari, “Local boosted features for illumination invariant face recognition,” , Imaging and Multimedia Analytics in a Web and Mobile World, Vol 4, 2017, pp. 70-73.
[15]	D. Socolinsky, A. Selinger, “Thermal face recognition in an operational scenario,” in: Proceedings of IEEE Conference on Pattern Recognition Compter Vision and Pattern Recognition, Vol. 2, 2004, pp. 1012-1019.
[16]	R. S. Ghiass, O. Arandjelovic, H. Bendada, X. Maldague, “Illumination-invariant face recognition from a single image across extreme pose using a dual dimension aam ensemble in the thermal infrared spectrum,” in: International Joint Conference on Neural Network, 2013.
[17]	Shwetank, Neeraj, Jitendra, Vikesh, , “Pixel based supervised classification of hyperspectral face images for face recognition,” , , 2018, pp. 706-717.
[18]	G. Hermosilla, J. R. del Solar, R. Verschae, M. Correa, “A comparative study of thermal face recognition methods in unconstrained environments,” Pattern Recognition 45 (2012) 2445-2459.
[19]	R. S. Ghiass, O. Arandjelovic, A. Bendada, X. Maldague, “Infrared face recognition: A comprehensive review of methodologies and databases,” Pattern Recognition 47 (2014) 2807-2824.
[20]	R. S. Choras, “Thermal face recognition,” in: Image Processing and Communications Challenges 7, 2016, pp. 37-46.
[21]	Z. Pan, G. Healey, M. Prasad, B. Tromberg, “Face recognition in hyperspectral images,” IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (12) (2003) 1552-1560.
[22]	Z. Pan, G. Healey, M. Prasad, B. Tromberg, “Recognizing faces in hyperspectral image,” in: Proceedings of the SPIE, Vol. 4725, 2002, pp. 168-176.
[23]	S. A. Robila, “Toward hyperspectral face recognition,” in: Proceedings of the SPIE, Vol. 6812, 2008.
[24]	C. P. Huynh, A. Robles-Kelly, “Hyperspectral imaging for skin recognition and biometrics,” in: Proceedings of IEEE Conference on Image Processing, 2010, p. 23252328.
[25]	A. Wimberly, S. A. Robila, T. Peplau, “Spectral face recognition using orthogonal subspace bases,” in: Proceedings of the SPIE, Vol. 7695, 2010.
[26]	Z. Pan, G. Healey, B. J. Tromberg, “Hyperspectral face recognition under unknown illumination,” Optical Engineering 46 (7).
[27]	H. Wang, G. Healey, “Pose-invariant face recognition in hyperspectral images,” in: Proceedings of the Image Processing and Computer Vision, 2013.
[28]	H. Wang, T. C. Bau, G. Healey, “Expression-invariant face recognition in hyperspectral images,” in: Proceedings of the SPIE, 2011.
[29]	L. Shen, S. Zheng, “Hyperspectral face recognition using 3d gabor wavelets,” in: Proceedings of the International Conference on Pattern Recognition, 2012.
[30]	M. Uzair, A. Mahmood, A. Mian, “Hyperspectral face recognition with spatiospectral information fusion and pls regression,” IEEE Transactions on Image Process 24 (2015) 1127-1137.
[31]	H. Drira, B. Amor, A. Srivastava, M. Daoudi, R. Slama, “3D face recognition under expressions, occlusions, and pose variations,” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013) 2270-2283.
[32]	R. Liang, W. Shen, X.-X. Li, H. Wang, “Bayesian multidistribution-based discriminative feature extraction for 3D face recognition,” Information Sciences 320 (2015) 406-417.
[33]	M. Hiraoka, M. Firbank, M. Essenpreis, M. Cope, S. Arridge, P. van der Zee, D. Delpy, “A monte carlo investigation of optical pathlength in inhomogeneous tissue and its application to nearinfrared spectroscopy,” Physics in Medicine and Biology 38 (12) (1994) 1859-1876.
[34]	A. Berk, G. Anderson, P. Acharya, L. Bernstein, L. Muratov, J. Lee, M. Fox, S. Adler-Golden, J. Chetwynd, M. Hoke, R. Lockwood, J. Gardner, T. Cooley, C. Borel, P. Lewis, E. Shettle, “Modtran5: 2006 update,” in: Proceedings of the SPIE, Vol. 6233, 2006.
[35]	D. Judd, D. MacAdam, G. Wyszecki, “Spectral distribution of typical daylight as a function of correlated color temperature,” Journal of the Optical Society of America 54 (8) (1964) 1031-1040.
[36]	D. Slater, G. Healey, “Analyzing the spectral dimensionality of outdoor visible and near-infrared illumination functions,” Journal of the Optical Society of America 15 (11) (1998) 2913-2920.
[37]	A. Georghiades, P. Belhumeur, D. J. Kriegman, “From few to many: illumination cone models for face recognition under differing pose and lighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (6) (2001) 643-660.
[38]	J. R. Beveridge, D. S. Bolme, M. Teixeira, B. Draper, “The csu face identification evaluation system users guide: version 5.0,” in: Technical Report, Computer Science Department, Colorado State University, 2003.
[39]	D. Bolme, J. R. Beveridge, M. Teixeira, B. A. Draper, “The csu face identification evaluation system: its purpose, features and structure,” in: Proceedings of the International Conference on Computer Vision Systems, no. 304-311, 2003.