Gait recognition with hybrid and ensemble deep learning models

Gait recognition is a biometric technology that identifies individuals based on their unique walking patterns. The distinctiveness of a person’s gait arises from various traits such as stride length, arm swing, limb movement, and others, making it challenging to conceal or imitate. Furthermore, gait...

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Main Author:	Mogan, Jashila Nair
Format:	Thesis
Published:	2023
Subjects:	TK7800-8360 Electronics
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id	my-mmu-ep.12884
record_format	uketd_dc
spelling	my-mmu-ep.128842024-08-29T05:22:12Z Gait recognition with hybrid and ensemble deep learning models 2023-10 Mogan, Jashila Nair TK7800-8360 Electronics Gait recognition is a biometric technology that identifies individuals based on their unique walking patterns. The distinctiveness of a person’s gait arises from various traits such as stride length, arm swing, limb movement, and others, making it challenging to conceal or imitate. Furthermore, gait recognition does not require any active participation from the subject, rendering it a convenient method of identification. However, gait recognition performance can be influenced by several covariates, including walking speed, viewing angle, clothing, and carrying conditions. These factors can alter the appearance of gait patterns, complicating accurate identification. To address these issues,a gait representation named “windowed GEI” alongside five deep learning-based models are presented: Gait-DenseNet201-MLP, Gait-VGG16-MLP, Gait-ViT-MLP, ensemble CNN-ViT with decision-level fusion, and ensemble CNNViT with feature-level fusion. The windowed GEI involves averaging the gait frames without regard to gait cycle. This technique helps alleviate potential biases associated with gait cycles, leading to improved generalisation of the gait representation. The Gait-DenseNet201-MLP utilises a pre-trained DenseNet201 model combined with a Multilayer Perceptron (MLP). All prior layers are connected to subsequent layers, enhancing the information flow among layers and promoting feature learning. The Gait-VGG16-MLP model employs the VGG16 architecture and reduces the number of parameters involved by using the smallest kernel size, thereby significantly decreasing execution time. The third model, Gait-ViT-MLP, is a Transformer-based approach that leverages the attention mechanism to focus on significant regions of an image, substantially improving performance. As each individual model possesses unique strengths, an ensemble model combining the predictions of these three models is proposed to further enhance performance. It is evident that the ensemble model significantly boosts performance compared to individual models. The final model fuses the representation of the models. The fusion yields a more comprehensive and informative feature representation, which effectively amplifies the performance. The proposed models are evaluated on several gait datasets, including the OU-ISIR treadmill gait dataset D, CASIA dataset B, and OU-ISIR large population dataset. 2023-10 Thesis https://shdl.mmu.edu.my/12884/ http://erep.mmu.edu.my/ phd doctoral Multimedia University Faculty of Information Science and Technology (FIST) EREP ID: 12308
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Mogan, Jashila Nair Gait recognition with hybrid and ensemble deep learning models
description	Gait recognition is a biometric technology that identifies individuals based on their unique walking patterns. The distinctiveness of a person’s gait arises from various traits such as stride length, arm swing, limb movement, and others, making it challenging to conceal or imitate. Furthermore, gait recognition does not require any active participation from the subject, rendering it a convenient method of identification. However, gait recognition performance can be influenced by several covariates, including walking speed, viewing angle, clothing, and carrying conditions. These factors can alter the appearance of gait patterns, complicating accurate identification. To address these issues,a gait representation named “windowed GEI” alongside five deep learning-based models are presented: Gait-DenseNet201-MLP, Gait-VGG16-MLP, Gait-ViT-MLP, ensemble CNN-ViT with decision-level fusion, and ensemble CNNViT with feature-level fusion. The windowed GEI involves averaging the gait frames without regard to gait cycle. This technique helps alleviate potential biases associated with gait cycles, leading to improved generalisation of the gait representation. The Gait-DenseNet201-MLP utilises a pre-trained DenseNet201 model combined with a Multilayer Perceptron (MLP). All prior layers are connected to subsequent layers, enhancing the information flow among layers and promoting feature learning. The Gait-VGG16-MLP model employs the VGG16 architecture and reduces the number of parameters involved by using the smallest kernel size, thereby significantly decreasing execution time. The third model, Gait-ViT-MLP, is a Transformer-based approach that leverages the attention mechanism to focus on significant regions of an image, substantially improving performance. As each individual model possesses unique strengths, an ensemble model combining the predictions of these three models is proposed to further enhance performance. It is evident that the ensemble model significantly boosts performance compared to individual models. The final model fuses the representation of the models. The fusion yields a more comprehensive and informative feature representation, which effectively amplifies the performance. The proposed models are evaluated on several gait datasets, including the OU-ISIR treadmill gait dataset D, CASIA dataset B, and OU-ISIR large population dataset.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mogan, Jashila Nair
author_facet	Mogan, Jashila Nair
author_sort	Mogan, Jashila Nair
title	Gait recognition with hybrid and ensemble deep learning models
title_short	Gait recognition with hybrid and ensemble deep learning models
title_full	Gait recognition with hybrid and ensemble deep learning models
title_fullStr	Gait recognition with hybrid and ensemble deep learning models
title_full_unstemmed	Gait recognition with hybrid and ensemble deep learning models
title_sort	gait recognition with hybrid and ensemble deep learning models
granting_institution	Multimedia University
granting_department	Faculty of Information Science and Technology (FIST)
publishDate	2023
_version_	1811768016641196032

Gait recognition with hybrid and ensemble deep learning models

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