  Visible face recognition systems achieve nearly perfect recognition
accuracies using deep learning. However, in lack of light, these systems
perform poorly. A way to deal with this problem is thermal to visible
cross-domain face matching. This is a desired technology because of its
usefulness in night time surveillance. Nevertheless, due to differences between
two domains, it is a very challenging face recognition problem. In this paper,
we present a deep autoencoder based system to learn the mapping between visible
and thermal face images. Also, we assess the impact of alignment in thermal to
visible face recognition. For this purpose, we manually annotate the facial
landmarks on the Carl and EURECOM datasets. The proposed approach is
extensively tested on three publicly available datasets: Carl, UND-X1, and
EURECOM. Experimental results show that the proposed approach improves the
state-of-the-art significantly. We observe that alignment increases the
performance by around 2%. Annotated facial landmark positions in this study can
be downloaded from the following link:
github.com/Alpkant/Thermal-to-Visible-Face-Recognition-Using-Deep-Autoencoders .
