Visual BMI estimation from face images using a label distribution based method

Body mass index (BMI) analysis from face images is an interesting and challenging topic in machine learning and computer vision. Recent research shows that facial adiposity is associated with BMI prediction. In this work, we investigate the problem of visual BMI estimation from face images by a two-stage learning framework. BMI-related facial features are learned from the first stage. Then a label distribution based BMI estimator is learned by an optimization procedure that is implemented by projecting the features and assigned labels to a new domain which maximizing the correlation between them. Two label assignment strategies are analyzed for modeling the single BMI value as a discrete probability distribution over a range of BMIs. Extensive experiments are conducted on FIW-BMI, Morph II and VIP_attribute datasets. The experimental results show that the two-stage learning framework improves the performance step by step. More importantly, the proposed BMI estimator efficiently reduces the error. It outperforms regression based methods, two label distribution methods and two deep learning methods in most cases.