New Insights on Weight Estimation from Face Images

Abstract

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.