Static Hand Gesture Recognition Using Haar-Like Features

Hand gesture recognition plays a crucial role in communication between human and computer or robot. It is used to improve Human-Computer Interaction (HCI) for the sake of making the communication more natural and much easier. Static hand gesture or posture recognition using Haar-like features is bei...

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Main Author:	Wong, Kai Sin
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	https://eprints.ums.edu.my/id/eprint/30474/1/Static%20Hand%20Gesture%20Recognition%20Using%20Haar-Like%20Features%2024PAGES.pdf https://eprints.ums.edu.my/id/eprint/30474/2/Static%20Hand%20Gesture%20Recognition%20Using%20Haar-Like%20Features.pdf
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id	my-ums-ep.30474
record_format	uketd_dc
spelling	my-ums-ep.304742021-09-06T07:19:41Z Static Hand Gesture Recognition Using Haar-Like Features 2015 Wong, Kai Sin QA75.5-76.95 Electronic computers. Computer science Hand gesture recognition plays a crucial role in communication between human and computer or robot. It is used to improve Human-Computer Interaction (HCI) for the sake of making the communication more natural and much easier. Static hand gesture or posture recognition using Haar-like features is being presented in this paper. Two static hand gestures which are index finger and fist are trained using Haar-like features algorithm. Index finger represents left click mouse event while fist represents right click mouse event. AdaBoost algorithm is applied in the training phase to increase accuracy and robustness of the system. Since this is a real-time system, built-in webcam is used to capture the image of the gesture. Brightness and distance are tested for evaluation of this system. Some static imported images are also tested. The experimental results show that both static hand gestures achieve the highest accuracy under a high degree (80%-100%) of brightness. Index finger and fist achieve 90.4% and 91.2% accuracy respectively under a high degree of brightness. The best distance is 80cm from the screen. Index finger achieves 92% accuracy for 80cm distance while the fist achieves 95.2% for both 80cm and 100cm distances. 2015 Thesis https://eprints.ums.edu.my/id/eprint/30474/ https://eprints.ums.edu.my/id/eprint/30474/1/Static%20Hand%20Gesture%20Recognition%20Using%20Haar-Like%20Features%2024PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/30474/2/Static%20Hand%20Gesture%20Recognition%20Using%20Haar-Like%20Features.pdf text en validuser dphil doctoral Universiti Malaysia Sabah Faculty of Science and Natural Resources
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Wong, Kai Sin Static Hand Gesture Recognition Using Haar-Like Features
description	Hand gesture recognition plays a crucial role in communication between human and computer or robot. It is used to improve Human-Computer Interaction (HCI) for the sake of making the communication more natural and much easier. Static hand gesture or posture recognition using Haar-like features is being presented in this paper. Two static hand gestures which are index finger and fist are trained using Haar-like features algorithm. Index finger represents left click mouse event while fist represents right click mouse event. AdaBoost algorithm is applied in the training phase to increase accuracy and robustness of the system. Since this is a real-time system, built-in webcam is used to capture the image of the gesture. Brightness and distance are tested for evaluation of this system. Some static imported images are also tested. The experimental results show that both static hand gestures achieve the highest accuracy under a high degree (80%-100%) of brightness. Index finger and fist achieve 90.4% and 91.2% accuracy respectively under a high degree of brightness. The best distance is 80cm from the screen. Index finger achieves 92% accuracy for 80cm distance while the fist achieves 95.2% for both 80cm and 100cm distances.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Wong, Kai Sin
author_facet	Wong, Kai Sin
author_sort	Wong, Kai Sin
title	Static Hand Gesture Recognition Using Haar-Like Features
title_short	Static Hand Gesture Recognition Using Haar-Like Features
title_full	Static Hand Gesture Recognition Using Haar-Like Features
title_fullStr	Static Hand Gesture Recognition Using Haar-Like Features
title_full_unstemmed	Static Hand Gesture Recognition Using Haar-Like Features
title_sort	static hand gesture recognition using haar-like features
granting_institution	Universiti Malaysia Sabah
granting_department	Faculty of Science and Natural Resources
publishDate	2015
url	https://eprints.ums.edu.my/id/eprint/30474/1/Static%20Hand%20Gesture%20Recognition%20Using%20Haar-Like%20Features%2024PAGES.pdf https://eprints.ums.edu.my/id/eprint/30474/2/Static%20Hand%20Gesture%20Recognition%20Using%20Haar-Like%20Features.pdf
_version_	1747836565723283456

Static Hand Gesture Recognition Using Haar-Like Features

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