Robust Face Recognition from NIR Dataset via Sparse Representation

Abstract:

A biometric identification system may be a pc application for mechanically distinctive or confirmative of an individual from a digital image or a video frame from a video supply. One in all the ways that to try and do this can be by examination designated face expression from the image and a facial information. This paper planned dynamic face recognition from near-infrared images by exploitation sparse representation classifier. Most of the prevailing datasets for facial expressions are captured in a very visible light spectrum. However, the visible light (VIS) will modify with time and placement, causing important variations in look and texture. This new framework was designed to attain strength to pose variation and occlusion and to resolve uncontrolled environmental illumination for reliable biometric identification. This paper gift a unique analysis on a dynamic facial features recognition, exploitation near-infrared (NIR) datasets and LBP(Local binary patterns) feature descriptors. It shows sensible and strong results against illumination variations by exploitation infrared imaging system.

• Periodical: Applied Mechanics and Materials (Volume 573)
• Pages: 495-500
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.573.495
• Online since: June 2014
• Authors: M. Thenmozhi*, P. Gnanaskanda Parthiban
• Keywords: Corruption, Face Recognition, Feature Extraction, Local Binary Pattern (LBP), Near-Infrared (NIR), Occlusion, Sparse Representation, Visible Light (VIS)

References

[1] B. Fasel, Juergen Luettin. Automatic facial expression analysis: a survey,. Pattern Recognition 36 (2003) 259 – 275.

DOI: 10.1016/s0031-3203(02)00052-3

[2] Z. Zeng, M. Pantic, G.I. Roisman, and T.S. Huang, Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions, IEEE Trans. Pattern Analysis and Machine Intelligence 31(1) (2009)39–58.

DOI: 10.1109/tpami.2008.52

[3] Y. Adini, Y. Moses, and S. Ullman, Face recognition: The problem of Compensating for changes in illumination direction, IEEE Trans. Pattern Analysis and Machine Intelligence 19(7) (1997) 721–732.

DOI: 10.1109/34.598229

[4] L. Sirovich and M. Kirby, 'Low dimensional procedure for the characterization of human faces, J. Opt. Soc. Amer. A, vol. 4, no. 3, p.519–524, (1987).

[5] M. A. Turk and A. P. Pentland, Face recognition using eigenfaces, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1991, p.586–591.A. J. Patti.

DOI: 10.1109/cvpr.1991.139758

[6] J. Haddadnia, K. Faez, and P. Moallem, Human face recognition with moment invariants based on shape information, in Proceedings of the International Conference on Information Systems, Analysis and Synthesis, vol. 20, (Orlando, Florida USA), International Institute of Informatics and Systemics(ISAS), (2001).

DOI: 10.1109/icip.2001.959221

[7] D. Donoho, For Most Large Underdetermined Systems of Linear Equations the Minimal '1-Norm Near Solution Approximates the Sparest Solution, Comm. Pure and Applied Math., vol. 59, no. 10, 907-934, (2006).

DOI: 10.1002/cpa.20131

[8] P. Belhumeur, J. Hespanda, and D. Kriegman, Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp.711-720, July (1997).

DOI: 10.1109/34.598228

[9] A. Martinez, Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample perClass, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp.748-763, June (2002).

DOI: 10.1109/tpami.2002.1008382