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A novel deep network architecture for reconstructing RGB facial images from thermal for face recognition

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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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This work has been partially supported by IT Academy/, the Scientific and Technological Research Council of Turkey (TUBITAK) 1001 Project (116E097), by the Spanish project TIN2016-74946-P (MINECO/FEDER, UE) and CERCA Programme / Generalitat de Catalunya and the Estonian Centre of Excellence in IT (EXCITE) funded by the European Regional Development Fund. The authors also gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan X Pascal GPU. This work is partially supported by ICREA under the ICREA Academia programme.

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Correspondence to Gholamreza Anbarjafari.

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Litvin, A., Nasrollahi, K., Escalera, S. et al. A novel deep network architecture for reconstructing RGB facial images from thermal for face recognition. Multimed Tools Appl 78, 25259–25271 (2019).

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