@Article{AndreLitvin2019,
author="Andre Litvin
and Kamal Nasrollahi
and Sergio Escalera
and Cagri Ozcinar
and Thomas B. Moeslund
and Gholamreza Anbarjafari",
title="A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition",
journal="Multimedia Tools and Applications",
year="2019",
volume="78",
number="18",
pages="25259--25271",
optkeywords="Fully convolutional networks",
optkeywords="FusionNet",
optkeywords="Thermal imaging",
optkeywords="Face recognition",
abstract="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.",
optnote="HuPBA; no menciona",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3318), last updated on Fri, 21 Apr 2023 18:19:03 +0200",
opturl="https://link.springer.com/article/10.1007/s11042-019-7667-4"
}