Cross modal face matching between the thermal and visible spectrum is a much
de- sired capability for night-time surveillance and security applications. Due
to a very large modality gap, thermal-to-visible face recognition is one of the
most challenging face matching problem. In this paper, we present an approach
to bridge this modality gap by a significant margin. Our approach captures the
highly non-linear relationship be- tween the two modalities by using a deep
neural network. Our model attempts to learn a non-linear mapping from visible
to thermal spectrum while preserving the identity in- formation. We show
substantive performance improvement on a difficult thermal-visible face
dataset. The presented approach improves the state-of-the-art by more than 10%
in terms of Rank-1 identification and bridge the drop in performance due to the
modality gap by more than 40%.Comment: BMVC 2015 (oral