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Photometric normalization techniques for extended multi-spectral face recognition: A comparative analysis

Show simple item record Vetrekar, N.T. Raghavendra, R. Gad, R.S. Naik, G.M. 2017-11-06T06:24:17Z 2017-11-06T06:24:17Z 2017
dc.identifier.citation Computer Vision, Graphics, and Image Processing (ICVGIP 2016), Ed. by: Snehasis Mukherjee, Suvadip Mukherjee, Dipti Prasad Mukherjee, Jayanthi Sivaswamy, Suyash Awate, Srirangaraj Setlur, Anoop M. Namboodiri, Santanu Chaudhury. Lecture Notes in Computer Science. Springer. 10481; 2017; 27-38. en_US
dc.description.abstract Biometric authentication based on face recognition acquired enormous attention due to its non-intrusive nature of image capture. Recently, with the advancement in sensor technology, face recognition based on Multi-spectral imaging has gained lot of popularity due to its potential of capturing discrete spatio-spectral images across the electromagnetic spectrum. Our contribution here is to study empirically, the extensive comparative performance analysis of 22 photometric illumination normalization techniques for robust Multi-spectral face recognition. To evaluate this study, we developed a Multi-spectral imaging sensor that can capture Multi-spectral facial images across nine different spectral band in the wavelength range from 530 nm to 1000 nm. With the developed sensor we captured Multi-spectral facial database for 231 individuals, which will be made available in the public domain for the researcher community. Further, quantitative experimental performance analysis in the form of identification rate at rank 1, was conducted on 22 photometric normalization techniques using four state-of-the-art face recognition algorithms. The performance analysis indicates outstanding results with utmost all of the photometric normalization techniques for six spectral bands such as 650 nm, 710 nm, 770 nm, 830 nm, 890 nm, 950 nm. en_US
dc.publisher Springer en_US
dc.subject Electronics en_US
dc.title Photometric normalization techniques for extended multi-spectral face recognition: A comparative analysis en_US
dc.type Book chapter en_US

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