Improving face recognition via narrowband spectral range selection using Jeffrey divergence
IEEE Transactions on Information Forensics and Security
Fusing continuous spectral images for face recognition under indoor and outdoor illuminants
Machine Vision and Applications
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A novel physics-based fusion of multispectral images within the visual spectra is proposed for the purpose of improving face recognition under constant or varying illumination. Spectral images are fused according to the physics properties of the imaging system, including illumination, spectral response of the camera, and spectral reflectance of skin. The fused image is given as a probe to the recognition software FaceIt® which compares it to a gallery of images. The identification performance of our physics-based fusion method is compared to the performance of Principle Component Analysis and average fusion methods. The results show that the proposed fusion yields a higher identification rate. A method of illumination adjustment is proposed when the probe and gallery images are acquired under different illumination conditions. The results show that the identification rate is higher than that of unadjusted gray-level images.