AggreGait: Automatic gait feature extraction for human age and gender classification with possible occlusion
Year: 2025
Type: article
Abstract: The growing interest in smart surveillance and automated public access control necessitates robust age and gender classification (AGC) techniques that can operate effectively in unconstrained environments. While model-based gait obtained via pose estimation offers a promising approach, its performance can be hindered by occlusions commonly encountered in real-world videos. In this work, we propose a custom Graph Neural Network (GNN) architecture, AggreGait, for robust AGC under occlusions. AggreGait integrates upper and lower body features with whole-body information for age and gender prediction. We train AggreGait on pose sequences from the gait-in-the-wild (GITW) dataset, simulating different types of occlusions. AggreGait performs comparably to existing methods, achieving an overall accuracy of 91% in unobstructed conditions. Notably, AggreGait maintains reasonable accuracy using only upper limb (or upper and lower limb) features, suggesting its potential for real-time surveillance applications despite occlusions. This work paves the way for practical gait-based AGC in unconstrained environments, enhancing the effectiveness of surveillance systems and facilitating automated access control. • Custom Graph Neural Networks can automatically extract skeleton features for age and gender classification. • Body parts occlusion is a common problem in real-life surveillance. • Aggregating body part features makes age and gender classification possible, even with occlusion.
Source: Array
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,680
OpenAlex ID: W4408221224