Mutual Information Based Multispectral Image Fusion for Improved Face Recognition

Multispectral face images captured in more than one spectra is known to provide reliable person verification, especially in varying illumination conditions. In this paper, we present an extended multi-spectral face recognition framework by combining the face images captured in six different spectra consisting of 425nm,475nm,525nm,570nm,625nm, and 680nm. We propose a novel image fusion scheme that combines the information from different spectrum of multispectral face images. The proposed image fusion scheme first selects two images from set of all spectral images based on the highest information quantified using entropy measure. Two selected images are combined by decomposing them using Discrete Wavelet Transform (DWT) to get the sub-bands that are fused using weighted sum rule. The weights are computed automatically on each of these sub-bands by measuring the dependency using correlation and wavelet energy. Extensive experiments are carried out on a newly constructed exclusive multispectral face database using a commercial multispectral sensor SpectraCamTM from Pixelteq company. Extensive experiments are carried out on our database to present both qualitative and quantitative results of the proposed image fusion scheme. The comprehensive comparative analysis is performed by comparing the performance of the proposed scheme with four different state-of-the-art schemes. The obtained results have justified the efficacy of the proposed system for robust multispectral face recognition.