PT Journal
AU Andre Litvin
   Kamal Nasrollahi
   Sergio Escalera
   Cagri Ozcinar
   Thomas B. Moeslund
   Gholamreza Anbarjafari
TI A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition
SO Multimedia Tools and Applications
JI MTAP
PY 2019
BP 25259–25271
VL 78
IS 18
DE Fully convolutional networks; FusionNet; Thermal imaging; Face recognition
AB This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network.
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