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Communication Dans Un Congrès Année : 2023

New Insights on Weight Estimation from Face Images

Résumé

Weight is a soft biometric trait which estimation is useful in numerous health related applications such as remote estimation from a health professional or at-home daily monitoring. In scenarios when a scale is unavailable or the subject is unable to cooperate, i.e. road accidents, estimating a person's weight from face appearance allows for a contactless measurement. In this article, we define an optimal transfer learning protocol for a ResNet50 architecture obtaining better performances than the state-of-the-art thus moving one step forward in closing the gap between remote weight estimation and physical devices. We also demonstrate that gender-splitting, image cropping and hair occlusion play an important role in weight estimation which might not necessarily be the case in face recognition. We use up-to-date explainability tools to illustrate and validate our assumptions. We conduct extensive simulations on the most popular publicly available face dataset annotated by weight to ensure a fair comparison with other approaches and we aim to overcome its flaws by presenting our self-collected database composed of 400 new images.
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Dates et versions

hal-03948924 , version 1 (20-01-2023)

Identifiants

  • HAL Id : hal-03948924 , version 1

Citer

Nelida Mirabet-Herranz, Khawla Mallat, Jean-Luc Dugelay. New Insights on Weight Estimation from Face Images. FG 2023, IEEE International Conference on Automatic Face and Gesture Recognition, 5-8 January 2023, Waikoloa, Hawaii, Jan 2023, Waikoloa, Haiti. ⟨hal-03948924⟩

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