  In addition to body weight and Body Mass Index (BMI), body composition is an
essential data point that allows people to understand their overall health and
body fitness. However, body composition is largely made up of muscle, fat,
bones, and water, which makes estimation not as easy and straightforward as
measuring body weight. In this paper, we introduce a multimodal multi-task deep
neural network to estimate body fat percentage and skeletal muscle mass by
analyzing facial images in addition to a person's height, gender, age, and
weight information. Using a dataset representative of demographics in Japan, we
confirmed that the proposed approach performed better compared to the existing
methods. Moreover, the multi-task approach implemented in this study is also
able to grasp the negative correlation between body fat percentage and skeletal
muscle mass gain/loss.
