  Face recognition in real life situations like low illumination condition is
still an open challenge in biometric security. It is well established that the
state-of-the-art methods in face recognition provide low accuracy in the case
of poor illumination. In this work, we propose an algorithm for a more robust
illumination invariant face recognition using a multi-modal approach. We
propose a new dataset consisting of aligned faces of thermal and visual images
of a hundred subjects. We then apply face detection on thermal images using the
biggest blob extraction method and apply them for fusing images of different
modalities for the purpose of face recognition. An algorithm is proposed to
implement fusion of thermal and visual images. We reason for why relying on
only one modality can give erroneous results. We use a lighter and faster CNN
model called MobileNet for the purpose of face recognition with faster
inferencing and to be able to be use it in real time biometric systems. We test
our proposed method on our own created dataset to show that real-time face
recognition on fused images shows far better results than using visual or
thermal images separately.
