Heterogeneous face recognition aims to acknowledge faces across totally different sensor modalities. Typically, gallery
images are normal visible spectrum pictures, and probe images are infrared images / sketches or thermal images. Now a day’s
vital improvements in face recognition are obtained by CNNs, gained knowledge from massive training datasets. In this paper,
we are trying to find a match between a sketch with a digital photograph, a thermal image with a digital photograph, and an
infrared image with a digital photograph. We explored different machine learning methods to reduce the discrepancies between
the various modalities
