Face analysis via multispectral imaging is a relatively unexplored territory in face recognition research. The multispectral, multimodal and multi-illuminant IRIS-M3 database was acquired, indoors and outdoors, to pro...
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Face analysis via multispectral imaging is a relatively unexplored territory in face recognition research. The multispectral, multimodal and multi-illuminant IRIS-M3 database was acquired, indoors and outdoors, to promote research in this direction. In the database, each data record has images spanning all bands in the visible spectrum and one thermal image, acquired under different illumination conditions. The spectral power distributions of the lighting sources and daylight conditions are also encoded in the database. Multispectral fused images show improved face recognition performance compared to the visible monochromatic images. Galleries and probes were selected from the indoor and outdoor sections of the database to study the effects of data and decision fusion in the presence of lighting changes. Our experiments were validated by comparing cumulative match characteristics of monochromatic probes against multispectral probes obtained via multispectral fusion by averaging, principal component analysis, wavelet analysis, illumination adjustment and decision level fusion. In this effort, we demonstrate that spectral bands, either individually or fused by different techniques, provide better face recognition results with up to 78% improvement on conventional visible images.
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