Estimating Human Weight from A Single Image

Body weight as one of the biometric traits has been studied in both the forensic and medical domains. However, estimating weight directly from 2D images is particularly challenging, since the visual inspection is rather sensitive to the distance between the subject and camera, even for the frontal view images. In this case, the widely used Body Mass Index (BMI) which is associated with body height and weight can be employed as a measure of weight to indicate the health conditions. Previous works on the estimation of BMI have predominantly focused on using multiple 2D images, 3D images, or facial images, however, these cues are not always available. To address this issue, we explore the feasibility of obtaining BMI from a single 2D body image with a dual-branch regression framework proposed in this work. More specifically, the framework comprises an anthropometric feature computation branch and a deep learning-based feature extraction branch. One aggregation layer maps all the features to an estimated BMI value. In addition, a new public 2Dimage-to-BMI dataset is collected and released to facilitate the study, which contains 4189 images (1477 males and 2712 females) from around 3000 subjects with the attributes including gender, age, height, and weight. Extensive experiments confirm that the proposed framework combining anthropometric features and deep features outperforms the single-type feature approaches in most cases on BMI estimation.