Citation

Mogan, Jashila Nair and Lee, Chin Poo and Lim, Kian Ming (2024) Ensemble CNN-ViT Using Feature-Level Fusion for Gait Recognition. IEEE Access, 12. pp. 108573-108583. ISSN 2169-3536

Text Ensemble CNN-ViT Using Feature-Level Fusion for Gait Recognition.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Individual deep learning models showcase impressive performance; however, the capacity of a single model might fall short in capturing the full spectrum of intricate patterns present in the input data. Thus, relying solely on a single model may hamper the attainment of optimal results and broader generalization. In light of this, the paper presents an ensemble method that leverages the strengths of multiple Convolutional Neural Networks (CNNs) and Transformer models to elevate gait recognition performance. Additionally, a novel gait representation named windowed Gait Energy Image (GEI) is introduced, obtained by averaging gait frames irrespective of gait cycles. Firstly, the windowed GEI is input to the Convolutional Neural Networks and Transformer models to learn significant gait features. Each model is followed by a Multilayer Perceptron (MLP) to encode the relationship between the extracted features and corresponding class labels. Subsequently, the extracted gait features from each model are flattened and concatenated into a cohesive feature representation before passing through another MLP for subject classification. The performance of the proposed method was assessed on three datasets: OU-ISIR dataset D, CASIA-B, and OU-LP dataset. Experimental results demonstrated remarkable improvements compared to existing methods across all three datasets. The proposed method achieved accuracy rates of 100% on OU-ISIR D, 99.93% on CASIA-B, and 99.94% on OU-LP, showcasing the superior performance of the Ensemble CNN-ViT model using featurelevel fusion compared to state-of-the-art methods.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, ensemble, fusion, feature-fusion, gait, gait recognition.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Sep 2024 08:00
Last Modified:	02 Sep 2024 08:00
URII:	http://shdl.mmu.edu.my/id/eprint/12922

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