Citation

Aderinola, Timilehin B. and Tee, Connie and Ong, Thian Song and Teoh, Andrew Beng Jin and Goh, Michael Kah Ong (2024) Gait-based age group classification with adaptive Graph Neural Network. Engineering Applications of Artificial Intelligence, 137. p. 109081. ISSN 0952-1976

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Abstract

Deep learning techniques have recently been utilized for model-free age-associated gait feature extraction. However, acquiring model-free gait demands accurate pre-processing such as background subtraction, which is non-trivial in unconstrained environments. On the other hand, model-based gait can be obtained without background subtraction and is less affected by covariates. For model-based gait-based age group classification problems, present works rely solely on handcrafted features, where feature extraction is tedious and requires domain expertise. This paper proposes a deep learning approach to extract age-associated features from model-based gait for age group classification. Specifically, we first develop an unconstrained gait dataset called Multimedia University Gait Age and Gender dataset (MMU GAG). Next, the body joint coordinates are determined via pose estimation algorithms and represented as compact gait graphs via a novel part aggregation scheme. Then, a Part-AdaptIve Residual Graph Convolutional Neural Network (PairGCN) is designed for age-associated feature learning. Experiments suggest that PairGCN features are far more informative than handcrafted features, yielding up to 99% accuracy for classifying subjects as a child, adult, or senior in the MMU GAG dataset. These results suggest the feasibility of deploying Artificial Intelligence-enabled solutions for access control, surveillance, and law enforcement in unconstrained environments.

Item Type:	Article
Uncontrolled Keywords:	Age classification Feature extraction Graph neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Sep 2024 08:20
Last Modified:	02 Sep 2024 08:20
URII:	http://shdl.mmu.edu.my/id/eprint/12928

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