Leveraging Auxiliary Tasks for Height and Weight Estimation by Multi Task Learning

Height and weight, two of the most important biological characteristics of human body, play crucial roles in physical condition estimation. Height and weight estimation with single face image via deep convolutional neural network suffers from poor performance due to lack of labeled data. To address this issue, inspired by the relevance of gender, age, height and weight, we propose an auxiliary-task learning framework, employing multiple relevant tasks to improve the performance of primary tasks. Specifically, gender prediction and age estimation are utilized as auxiliary tasks to assist primary tasks (i.e., height and weight estimation) learning via deep residual auxiliary block. Experiments are conducted on the public VIP-attributes datasets and our private VIPL-MumoFace- WH datasets. Our method outperforms the baseline methods of hard parameter sharing in multi-task learning, demonstrating the effectiveness of auxiliary-task learning framework for height and weight estimation.