Citation

Hao, Teo Jia and Mogan, Jashila Nair (2026) Gait Recognition System Using VGG16-MLP. In: 14th International Conference on Information and Communication Technology, ICoICT 2026, 30 July 2026 - 31 July 2026, Bandung.

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Abstract

This study proposes a deep convolutional gait recognition framework based on a VGG-16 architecture enhanced with dropout regularization and a multilayer perceptron (MLP) classifier. The network learns discriminative spatiotemporal gait representations from 2D silhouette sequences, enabling robust identity recognition. The proposed model is evaluated on the CASIA-B benchmark dataset under multiple viewing angles and walking conditions, achieving a peak recognition accuracy of 99.78% under the optimized hyperparameter configuration which surpasses existing silhouette-based deep learning methods. These results demonstrate the effectiveness of regularized deep feature extraction for non-intrusive biometric identification in surveillance and security applications.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Gait, Gait Recognition, Silhouette
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:	02 Oct 2026 07:46
Last Modified:	02 Oct 2026 07:46
URII:	http://shdl.mmu.edu.my/id/eprint/16841

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