A Method for Robust Multispectral Face Recognition

Matching Short Wave InfraRed (SWIR) face images against a face gallery of color images is a very challenging task. The photometric properties of images in these two spectral bands are highly distinct. This work presents a new cross-spectral face recognition method that encodes both magnitude and phase of responses of a classic bank of Gabor filters applied to multi-spectral face images. Three local operators: Simplified Weber Local Descriptor, Local Binary Pattern, and Generalized Local Binary Pattern are involved. The comparison of encoded face images is performed using the symmetric Kullbuck-Leibler divergence. We show that the proposed method provides high recognition rates at different spectra (visible, Near InfraRed and SWIR). In terms of recognition rates it outperforms Faceit R ⃝G8, a commercial software distributed by L1.