VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/15/7639 |
| _version_ | 1797433004520374272 |
|---|---|
| author | Jashila Nair Mogan Chin Poo Lee Kian Ming Lim Kalaiarasi Sonai Muthu |
| author_facet | Jashila Nair Mogan Chin Poo Lee Kian Ming Lim Kalaiarasi Sonai Muthu |
| author_sort | Jashila Nair Mogan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:09:54Z |
| format | Article |
| id | doaj.art-da241764ed034ba9880208c22e68c7a3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:09:54Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-da241764ed034ba9880208c22e68c7a32023-12-01T22:50:21ZengMDPI AGApplied Sciences2076-34172022-07-011215763910.3390/app12157639VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer PerceptronJashila Nair Mogan0Chin Poo Lee1Kian Ming Lim2Kalaiarasi Sonai Muthu3Faculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Melaka 75450, MalaysiaGait 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.https://www.mdpi.com/2076-3417/12/15/7639gaitgait recognitiondeep learningpre-trained modelmultilayer perceptron |
| spellingShingle | Jashila Nair Mogan Chin Poo Lee Kian Ming Lim Kalaiarasi Sonai Muthu VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron Applied Sciences gait gait recognition deep learning pre-trained model multilayer perceptron |
| title | VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron |
| title_full | VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron |
| title_fullStr | VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron |
| title_full_unstemmed | VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron |
| title_short | VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron |
| title_sort | vgg16 mlp gait recognition with fine tuned vgg 16 and multilayer perceptron |
| topic | gait gait recognition deep learning pre-trained model multilayer perceptron |
| url | https://www.mdpi.com/2076-3417/12/15/7639 |
| work_keys_str_mv | AT jashilanairmogan vgg16mlpgaitrecognitionwithfinetunedvgg16andmultilayerperceptron AT chinpoolee vgg16mlpgaitrecognitionwithfinetunedvgg16andmultilayerperceptron AT kianminglim vgg16mlpgaitrecognitionwithfinetunedvgg16andmultilayerperceptron AT kalaiarasisonaimuthu vgg16mlpgaitrecognitionwithfinetunedvgg16andmultilayerperceptron |