Thermal to Visible Face Recognition Using Deep Autoencoders
This addresses a challenging problem in night time surveillance by enabling face recognition in low-light conditions, though it appears incremental as it builds on existing cross-domain matching methods.
The paper tackles the problem of thermal to visible cross-domain face recognition for night time surveillance by proposing a deep autoencoder-based system, which significantly improves state-of-the-art performance and shows that alignment increases performance by around 2%.
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 .