Abstract
Optimal spectral bands selection is a primordial step in multispectral images based systems for face recognition. In this context, we select the best spectral bands using a multilinear sparse decomposition based approach. Multispectral images of 35 subjects presenting 25 different lengths from 480nm to 720nm and three lighting conditions: fluorescent, Halogen and Sun light are groupped in a 3-mode face tensor T of size 35×25×2 . T is then decomposed using 3-mode SVD where three mode matrices for subjects, spectral bands and illuminations are sparsely determined. The 25×25 spectral bands mode matrix defines a sparse vector for each spectral band. Spectral bands having the sparse vectors with the lowest variation with illumination are selected as the best spectral bands. Experiments on two state-of-the-art algorithms, MBLBP and HGPP, showed the effectiveness of our approach for best spectral bands selection.
Chapter PDF
Similar content being viewed by others
References
Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)
May, P., Ehrlich, H.-C., Steinke, T.: ZIB Structure Prediction Pipeline: Composing a Complex Biological Workflow through Web Services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1148–1158. Springer, Heidelberg (2006)
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid Information Services for Distributed Resource Sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184. IEEE Press, New York (2001)
Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration. Technical report, Global Grid Forum (2002)
National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov
Zou, J., Ji, Q., Nagy, G.: A comparative study of local matching approach for face recognition. Trans. Img. Proc. 16(10), 2617–2628 (2007)
Ruiz-del Solar, J., Verschae, R., Correa, M.: Recognition of faces in unconstrained environments: A comparative study. EURASIP J. Adv. Signal Process 2009, 1:1–1:19 (2009)
Lei, Z., Liao, S., Jain, A.K., Li, S.Z.: Coupled discriminant analysis for heterogeneous face recognition. IEEE Transactions on Information Forensics and Security 7(6), 1707–1716 (2012)
Lei, Z., Pietikainen, M., Li, S.Z.: Learning discriminant face descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 99, 1 (2013)
Chen, J., Yi, D., Yang, J., Zhao, G., Li, S.Z., Pietikinen, M.: Learning mappings for face synthesis from near infrared to visual light images. In: CVPR, pp. 156–163 (2009)
Zhang, B., Shan, S., Chen, X., Gao, W.: Histogram of gabor phase patterns (hgpp): A novel object representation approach for face recognition. Trans. Img. Proc. 16(1), 57–68 (2007)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE T-PAMI 28, 2037–2041 (2006)
Vu, N.-S., Caplier, A.: Enhanced patterns of oriented edge magnitudes for face recognition and image matching. IEEE T-IP 21(3), 1352–1365 (2012)
Li, S.Z., Lei, Z., Ao, M.: The hfb face database for heterogeneous face biometrics research. In: 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS, in conjunction with CVPR 2009), pp. 1005–1010 (2009)
Shao, T., Wang, Y.: The face images fusion based on laplacian pyramid and lbp operator. In: 9th International Conference on Signal Processing, pp. 1165–1169 (2008)
Chang, H., Koschan, A., Abidi, B.: Fusing continuous spectral images for face recognition under indoor and outdoor illuminants. Machine Vision and Application 19(4), 1432–1769 (2008)
Buyssens, P., Revenu, M.: Ir and visible face identification via sparse representation. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–6 (2010)
Mangai, U., Samanta, S., Das, S., Chowdhury, P.R.: A survey of decision fusion and feature fusion strategies for pattern classification. IETE Tech. Rev. 27, 293–307 (2010)
Chang, H., Yao, Y., Koschan, A., Abidi, B., Abidi, M.: Improving face recognition via narrowband spectral range selection using jeffrey divergence. Trans. Info. For. Sec. 4(1), 111–122 (2009)
Bouchech, H.J., Foufou, S., Koschan, A., Abidi, M.: Studies on the effectiveness of multispectral images for face recognition: Comparative studies and new approaches. In: Proceeding of the IEEE SITIS Conference, Kyoto, Japan (2013)
Bouchech, H.J., Foufou, S., Koschan, A., Abidi, M.: Dynamic best spectral bands selection for face recognition. To appear in the Proc. of the 48 International Conference on Information Sciences and Systems (CISS 2014), Princeton, NJ, USA, March 19-21 (2014)
Qiu, Q., Chellappa, R.: Compositional Dictionaries for Domain Adaptive Face Recognition. CoRR abs/1308.0271 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bouchech, H.J., Foufou, S., Abidi, M. (2014). Multilinear Sparse Decomposition for Best Spectral Bands Selection. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds) Image and Signal Processing. ICISP 2014. Lecture Notes in Computer Science, vol 8509. Springer, Cham. https://doi.org/10.1007/978-3-319-07998-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-07998-1_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07997-4
Online ISBN: 978-3-319-07998-1
eBook Packages: Computer ScienceComputer Science (R0)