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Communication Dans Un Congrès Année : 2022

TFLD: Thermal Face and Landmark Detection for Unconstrained Cross-spectral Face Recognition

Résumé

Automated thermal-to-visible face recognition has received increased attention due to benefits related to lowlight applications. Towards improvement of related matching accuracy, we hereby present TFLD, a detector of face and landmarks operating in the thermal spectrum. Our proposed TFLD is based on the architecture of YOLOv5, integrating sequential modules for face and landmark detection. We introduce a thermal face restoration scheme, in order to enhance thermal image quality and hence detection accuracy. We address data scarcity by transferring landmarks in paired visible and thermal images. Our experimental results showcase that our proposed detector accurately detects faces, as well as landmarks in a wide range of adversarial conditions. Further, TFLD achieves promising results on three benchmark multi-spectral face and landmark datasets, namely ARL-VTF, SF-TL54 and RWTH-Aachen; thereby improving the matching accuracy in cross-spectral face recognition by providing robust face alignment based on estimated facial landmarks.
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hal-03936331 , version 1 (12-01-2023)

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  • HAL Id : hal-03936331 , version 1

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David Anghelone, Sarah Lannes, Valeriya Strizhkova, Philippe Faure, Cunjian Chen, et al.. TFLD: Thermal Face and Landmark Detection for Unconstrained Cross-spectral Face Recognition. IJCB 2022 - IEEE International joint conference on biometrics, Oct 2022, Abu Dhabi, United Arab Emirates. ⟨hal-03936331⟩
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