Gait Recognition Using Deep Convolutional Features And Hash Coding

Human gait recognition is the tracking of the people by the way theywalk and the recognizing of human motion. It has gained attention because of its non-invasive and unobtrusive behaviors as well as its applicability in the various areas. Gait recognition is perceived as the most promising biometric...

Full description

Saved in:

Bibliographic Details
Main Author:	Pa, Pa Min
Format:	Thesis
Published:	2020
Subjects:	TK7800-8360 Electronics
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-mmu-ep.11419
record_format	uketd_dc
spelling	my-mmu-ep.114192023-05-22T06:06:29Z Gait Recognition Using Deep Convolutional Features And Hash Coding 2020-11 Pa, Pa Min TK7800-8360 Electronics Human gait recognition is the tracking of the people by the way theywalk and the recognizing of human motion. It has gained attention because of its non-invasive and unobtrusive behaviors as well as its applicability in the various areas. Gait recognition is perceived as the most promising biometric approach for the next coming decades, especially because of their efficiency and applicability in a surveillance system. Gait recognition becomes a challenging task in improving accuracy because of its covariant factors such as views, speed, and walking conditions. Due to the recent increase in gait data across surveillance systems, a rapid search for the required data becomes an emerging need. Researchers tend to pay less attention in addressing the issue of retrieval problem. The gait retrieval problem is to search the target individual in large datasets in an accurate manner. Hence, the objective of this study is to propose an efficient gait recognition framework to address the gait retrieval problem in a largescale dataset. The deep gait retrieval hashing model (DGRH) is proposed to retrieve the similar gait person from the large-scale dataset given by the query subject. The DGRH model uses the deep supervised hashing approach, with a convolutional neural network. Hence, the suitable CNN architecture for the gait feature extraction is proposed. The three CNN architectures with different activation functions are studied and their performances are analyzed in gait classification and verification framework. The classification model, with different architectures is evaluated in terms of correct classification rate (CCR). For the verification framework, the proposed CNN architectures are built in the Siamese network to address the one-to-one association. The performances are evaluated against the equal error rate (EER). The DGRH model uses the convolutional neural network to learn the features representation and the last layer of the architecture generated the hash codes. The hash function is learned by optimizing the classification loss, which is the responsibility for the similaritypreserving with gait labels and quantization loss for the hash quality. The ability of CNN is able to generate quality hash codes. The gait retrieval was performed withthe Hamming distance between the database hash codes and query hash codes. The proposed framework is evaluated against the three public datasets (CASIA-B, OUISIR-Large Population Dataset, OUISIR Multi-view Large Population Dataset) with cross-walking conditions in terms of mean average precision (MAP) and precision curves based on the top-returned images. 2020-11 Thesis http://shdl.mmu.edu.my/11419/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Information Science and Technology (FIST) EREP ID: 9897
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Pa, Pa Min Gait Recognition Using Deep Convolutional Features And Hash Coding
description	Human gait recognition is the tracking of the people by the way theywalk and the recognizing of human motion. It has gained attention because of its non-invasive and unobtrusive behaviors as well as its applicability in the various areas. Gait recognition is perceived as the most promising biometric approach for the next coming decades, especially because of their efficiency and applicability in a surveillance system. Gait recognition becomes a challenging task in improving accuracy because of its covariant factors such as views, speed, and walking conditions. Due to the recent increase in gait data across surveillance systems, a rapid search for the required data becomes an emerging need. Researchers tend to pay less attention in addressing the issue of retrieval problem. The gait retrieval problem is to search the target individual in large datasets in an accurate manner. Hence, the objective of this study is to propose an efficient gait recognition framework to address the gait retrieval problem in a largescale dataset. The deep gait retrieval hashing model (DGRH) is proposed to retrieve the similar gait person from the large-scale dataset given by the query subject. The DGRH model uses the deep supervised hashing approach, with a convolutional neural network. Hence, the suitable CNN architecture for the gait feature extraction is proposed. The three CNN architectures with different activation functions are studied and their performances are analyzed in gait classification and verification framework. The classification model, with different architectures is evaluated in terms of correct classification rate (CCR). For the verification framework, the proposed CNN architectures are built in the Siamese network to address the one-to-one association. The performances are evaluated against the equal error rate (EER). The DGRH model uses the convolutional neural network to learn the features representation and the last layer of the architecture generated the hash codes. The hash function is learned by optimizing the classification loss, which is the responsibility for the similaritypreserving with gait labels and quantization loss for the hash quality. The ability of CNN is able to generate quality hash codes. The gait retrieval was performed withthe Hamming distance between the database hash codes and query hash codes. The proposed framework is evaluated against the three public datasets (CASIA-B, OUISIR-Large Population Dataset, OUISIR Multi-view Large Population Dataset) with cross-walking conditions in terms of mean average precision (MAP) and precision curves based on the top-returned images.
format	Thesis
qualification_level	Master's degree
author	Pa, Pa Min
author_facet	Pa, Pa Min
author_sort	Pa, Pa Min
title	Gait Recognition Using Deep Convolutional Features And Hash Coding
title_short	Gait Recognition Using Deep Convolutional Features And Hash Coding
title_full	Gait Recognition Using Deep Convolutional Features And Hash Coding
title_fullStr	Gait Recognition Using Deep Convolutional Features And Hash Coding
title_full_unstemmed	Gait Recognition Using Deep Convolutional Features And Hash Coding
title_sort	gait recognition using deep convolutional features and hash coding
granting_institution	Multimedia University
granting_department	Faculty of Information Science and Technology (FIST)
publishDate	2020
_version_	1776101404098691072

Gait Recognition Using Deep Convolutional Features And Hash Coding

Similar Items