Journal
APPLIED SCIENCES-BASEL
Volume 12, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/app12157639
Keywords
gait; gait recognition; deep learning; pre-trained model; multilayer perceptron
Categories
Funding
- Fundamental Research Grant Scheme of the Ministry of Higher Education [FRGS/1/2021/ICT02/MMU/02/4]
- Multimedia University Internal Research Grant [MMUI/220021]
- Yayasan Universiti Multimedia [MMU/YUM/C/2019/YPS]
Ask authors/readers for more resources
This paper proposes a gait recognition method that combines a pre-trained VGG-16 model with a multilayer perceptron to identify different subjects based on gait energy images and gait features. Experiments show that the proposed method outperforms existing methods on multiple datasets.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available