Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset
Human gait recognition (HGR) has been employed as a biometric technique for security purposes over the last decade. Various factors, including clothing, carrying items, and walking surfaces, can influence the performance of gait recognition. Additionally, identifying individuals from different viewp...
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MDPI AG
2024-12-01
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| Series: | Technologies |
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| author | Nakib Aman Md. Rabiul Islam Md. Faysal Ahamed Mominul Ahsan |
| author_facet | Nakib Aman Md. Rabiul Islam Md. Faysal Ahamed Mominul Ahsan |
| author_sort | Nakib Aman |
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| description | Human gait recognition (HGR) has been employed as a biometric technique for security purposes over the last decade. Various factors, including clothing, carrying items, and walking surfaces, can influence the performance of gait recognition. Additionally, identifying individuals from different viewpoints presents a significant challenge in HGR. Numerous conventional and deep learning techniques have been introduced in the literature for HGR, but traditional methods are not well suited to handling large datasets. This research explores the effectiveness of four deep learning models for gait identification in the CASIA B dataset: the convolutional neural network (CNN), multi-layer perceptron (MLP), self-organizing map (SOMs), and transfer learning with EfficientNet. The selected deep learning techniques offer robust feature extraction and the efficient handling of large datasets, making them ideal in enhancing the accuracy of gait recognition. The collection includes gait sequences from 10 individuals, with a total of 92,596 images that have been reduced to 64 × 64 pixels for uniformity. A modified model was developed by integrating sequential convolutional layers for detailed spatial feature extraction, followed by dense layers for classification, optimized through rigorous hyperparameter tuning and regularization techniques, resulting in an accuracy of 97.12% for the test set. This work enhances our understanding of deep learning methods in gait analysis, offering significant insights for choosing optimal models in security and surveillance applications. |
| format | Article |
| id | doaj-art-200c4498d3ee4f99844f8aa1af23d73b |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-200c4498d3ee4f99844f8aa1af23d73b2024-12-27T14:56:04ZengMDPI AGTechnologies2227-70802024-12-01121226410.3390/technologies12120264Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B DatasetNakib Aman0Md. Rabiul Islam1Md. Faysal Ahamed2Mominul Ahsan3Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, BangladeshDepartment of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, BangladeshDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, BangladeshDepartment of Computer Science, University of York, Deramore Lane, York YO10 5GH, UKHuman gait recognition (HGR) has been employed as a biometric technique for security purposes over the last decade. Various factors, including clothing, carrying items, and walking surfaces, can influence the performance of gait recognition. Additionally, identifying individuals from different viewpoints presents a significant challenge in HGR. Numerous conventional and deep learning techniques have been introduced in the literature for HGR, but traditional methods are not well suited to handling large datasets. This research explores the effectiveness of four deep learning models for gait identification in the CASIA B dataset: the convolutional neural network (CNN), multi-layer perceptron (MLP), self-organizing map (SOMs), and transfer learning with EfficientNet. The selected deep learning techniques offer robust feature extraction and the efficient handling of large datasets, making them ideal in enhancing the accuracy of gait recognition. The collection includes gait sequences from 10 individuals, with a total of 92,596 images that have been reduced to 64 × 64 pixels for uniformity. A modified model was developed by integrating sequential convolutional layers for detailed spatial feature extraction, followed by dense layers for classification, optimized through rigorous hyperparameter tuning and regularization techniques, resulting in an accuracy of 97.12% for the test set. This work enhances our understanding of deep learning methods in gait analysis, offering significant insights for choosing optimal models in security and surveillance applications.https://www.mdpi.com/2227-7080/12/12/264gait recognitiondeep learningconvolutional neural networkmulti-layer perceptronself-organizing maptransfer learning |
| spellingShingle | Nakib Aman Md. Rabiul Islam Md. Faysal Ahamed Mominul Ahsan Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset Technologies gait recognition deep learning convolutional neural network multi-layer perceptron self-organizing map transfer learning |
| title | Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset |
| title_full | Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset |
| title_fullStr | Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset |
| title_full_unstemmed | Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset |
| title_short | Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset |
| title_sort | performance evaluation of various deep learning models in gait recognition using the casia b dataset |
| topic | gait recognition deep learning convolutional neural network multi-layer perceptron self-organizing map transfer learning |
| url | https://www.mdpi.com/2227-7080/12/12/264 |
| work_keys_str_mv | AT nakibaman performanceevaluationofvariousdeeplearningmodelsingaitrecognitionusingthecasiabdataset AT mdrabiulislam performanceevaluationofvariousdeeplearningmodelsingaitrecognitionusingthecasiabdataset AT mdfaysalahamed performanceevaluationofvariousdeeplearningmodelsingaitrecognitionusingthecasiabdataset AT mominulahsan performanceevaluationofvariousdeeplearningmodelsingaitrecognitionusingthecasiabdataset |