Citation

Chang, Zi Jin and Connie, Tee and Teoh, Andrew Beng Jin and Goh, Michael Kah Ong (2026) MAXGait: A hybrid Mamba-attentions solution for soft biometrics analysis using gait patterns. Array, 30. p. 100944. ISSN 25900056

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Abstract

Recent advances in sequence modeling have resulted in powerful architectures for discovering patterns across different data types; however, heavyweight Transformers are excessive for signals like 3D skeleton gait. Motivated by this mismatch, we seek models that strike a balance between temporal expressiveness and computational efficiency. We introduce MAXGait, a lightweight network for soft-biometric inference that predicts age and gender from sequences of 3D joint coordinates. Built on Mamba, MAXGait utilizes selective state-space dynamics to efficiently capture long-range dependencies in joint trajectories. Moving away from the original design, MAXGait features a compact topology that maintains accuracy within tight resource constraints. To enhance representation, we develop Multi-Hierarchical Focusing Attention, which jointly models micro-motions and global gait cycles in the skeleton. Extensive experiments demonstrate that MAXGait is competitive with state-ofthe-art baselines for age–gender classification on extended 3D skeleton sequences. Specifically, MAXGait achieves an overall accuracy and macro-F1 score of 72.77% and 72.5% on our self-curated Nano3Dgait dataset under a subject-overlapping protocol, 83.15% and 66.17% on OUMVLP-Mesh, and 88.62% and 84% on CASIA-B. It also offers over 20% efficiency gains and a smaller parameter footprint compared to a similarly compressed vanilla Mamba, while maintaining strong predictive performance. Yet, the significant disparity in sample sizes between groups still poses a serious challenge to unbiased performance, with the lowest per-group F1-score dropping to 33% under severely underrepresented conditions.

Item Type:	Article
Uncontrolled Keywords:	Soft biometrics, gait
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 Rosnani Abd Wahab
Date Deposited:	01 Jul 2026 01:48
Last Modified:	01 Jul 2026 01:48
URII:	http://shdl.mmu.edu.my/id/eprint/16171

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