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Thermal to Visible Face Recognition Using Deep Autoencoders
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Datum
2019
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Gesellschaft für Informatik e.V.
Zusammenfassung
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 .