Citation

Aderinola, Timilehin B. and Connie, Tee and Ong, Thian Song and Teoh, Andrew Beng Jin and Goh, Michael Kah Ong (2025) AggreGait: Automatic gait feature extraction for human age and gender classification with possible occlusion. Array, 26. p. 100379. ISSN 25900056

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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.

Item Type:	Article
Uncontrolled Keywords:	Age group classification, Gait, Gender classification, Occlusion
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	29 Apr 2025 08:33
Last Modified:	29 Apr 2025 08:33
URII:	http://shdl.mmu.edu.my/id/eprint/13692

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