VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron

Citation

Mogan, Jashila Nair and Lee, Chin Poo and Lim, Kian Ming and Muthu Anbananthen, Kalaiarasi Sonai (2022) VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron. Applied Sciences, 12 (15). p. 7639. ISSN 2076-3417

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Abstract

Gait is a pattern of a person’s walking. The body movements of a person while walking makes the gait unique. Regardless of the uniqueness, the gait recognition process suffers under various factors, namely the viewing angle, carrying condition, and clothing. In this paper, a pre-trained VGG-16 model is incorporated with a multilayer perceptron to enhance the performance under various covariates. At first, the gait energy image is obtained by averaging the silhouettes over a gait cycle. Transfer learning and fine-tuning techniques are then applied on the pre-trained VGG-16 model to learn the gait features of the attained gait energy image. Subsequently, a multilayer perceptron is utilized to determine the relationship among the gait features and the corresponding subject. Lastly, the classification layer identifies the corresponding subject. Experiments are conducted to evaluate the performance of the proposed method on the CASIA-B dataset, the OU-ISIR dataset D, and the OU-ISIR large population dataset. The comparison with the state-of-the-art methods shows that the proposed method outperforms the methods on all the datasets.

Item Type: Article
Uncontrolled Keywords: Gait recognition, deep learning, pre-trained model, multilayer perceptron
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: 22 Sep 2022 02:19
Last Modified: 22 Sep 2022 02:19
URII: http://shdl.mmu.edu.my/id/eprint/10452

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