GaitAGE: Gait Age and Gender Estimation Based on an Age- and Gender-Specific 3D Human Model

Abstract

Gait-based human age and gender estimation has potential applications in visual surveillance, such as searching for specific pedestrian groups and automatically counting customers by different ages/genders. Unlike most existing methods that exploit widely used appearance-based gait features (e.g., gait energy image and silhouettes) or simple model-based gait features (e.g., leg length, stride width/frequency, and head-to-body ratio), we explore a recently popular 3D human mesh model (i.e., skinned multi-person linear model (SMPL)), which is more robust to various covariates (e.g., view angles). Furthermore, instead of the commonly used gender-neutral SMPL model, we propose a simple yet effective method to generate more realistic age- and gender-specific human mesh models by interpolating among male, female, and infant SMPL models using two learned age and gender weights. The age weight controls the proportion of importance between male/female and infant models, which is learned in a data-driven scheme by considering the paired relation between ground-truth ages and age weights. The gender weight controls the proportion of importance between male and female models, which indicates the gender probability. Then, we explore the use of generated realistic mesh models for age and gender estimation. Finally, the human mesh reconstruction and age and gender estimation modules are integrated into a unified end-to-end framework for training and testing. The experimental results on the OU-MVLP and FVG datasets demonstrated that the proposed method achieved both good mesh reconstruction and state-of-the-art age and gender estimation results.