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Communication Dans Un Congrès Année : 2021

Explainable Thermal to Visible Face Recognition Using Latent-Guided Generative Adversarial Network

Résumé

One of the main challenges in performing thermalto-visible face image translation is preserving the identity across different spectral bands. Existing work does not effectively disentangle the identity from other confounding factors. In this paper, we propose a Latent-Guided Generative Adversarial Network (LG-GAN) to explicitly decompose an input image into identity code that is spectral-invariant and style code that is spectral-dependent. By using such a disentanglement, we are able to analyze the identity preservation by interpreting and visualizing the identity code. We present extensive face recognition experiments on two challenging Visible-Thermal face datasets. We show that the learned identity code is effective in preserving the identity, thus offering useful insights on interpreting and explaining thermal-to-visible face image translation.
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Dates et versions

hal-03523037 , version 1 (12-01-2022)

Identifiants

Citer

David Anghelone, Cunjian Chen, Philippe Faure, Arun Ross, Antitza Dantcheva. Explainable Thermal to Visible Face Recognition Using Latent-Guided Generative Adversarial Network. FG 2021 - IEEE International Conference on Automatic Face and Gesture Recognition, Dec 2021, Jodhpur, India. ⟨10.1109/FG52635.2021.9667018⟩. ⟨hal-03523037⟩
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