Three-dimension deep model for body mass index estimation from facial image sequences with different poses

Abstract

Body mass index (BMI), an essential indicator of human health, can be calculated based on height and weight. Previous studies have carried out visual BMI estimation from a frontal facial image. However, these studies have ignored the visual information provided by the different face poses on BMI estimation. Considering the contributions of different face poses, this study applies the perspective transformation to the public facial image dataset to simulate face rotation and collects a video dataset with face rotation in yaw type. A three-dimensional convolutional neural network, which integrates the facial three-dimensional information from an image sequence with different face poses, is proposed for BMI estimation. The proposed methods are validated using the public and private datasets. Ablation experiments demonstrate that the face sequence with different poses can improve the performance of visual BMI estimation. Comparison experiments indicate that the proposed method can increase classification accuracy and reduce visual BMI estimation errors. Code has been released: https://github.com/xiangch1910/STNET-BMI.