Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The main objective of this effort is to explore the utility of two feature extraction methods, namely, hand contour and complex m...
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| Format: | Thesis |
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2014
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| Online Access: | http://studentsrepo.um.edu.my/4744/ http://studentsrepo.um.edu.my/4744/1/Thesis_%2D_Haitham.pdf |
| _version_ | 1848772710414417920 |
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| author | Hasan, Haitham Sabah |
| author_facet | Hasan, Haitham Sabah |
| author_sort | Hasan, Haitham Sabah |
| building | UM Research Repository |
| collection | Online Access |
| description | The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The main objective of this effort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Artificial neural network is built for the purpose of classification by using the back- propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classification. In the pre-processing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The back-propagation learning algorithm is employed in the multi-layer neural network classifier. The results show that hand contour method has a performance of 71.30% recognition, while complex moments has a better performance of 86.90% recognition rate. |
| first_indexed | 2025-11-14T13:30:50Z |
| format | Thesis |
| id | um-4744 |
| institution | University Malaya |
| institution_category | Local University |
| last_indexed | 2025-11-14T13:30:50Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | um-47442015-02-18T04:14:10Z Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan Hasan, Haitham Sabah QA75 Electronic computers. Computer science T Technology (General) The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The main objective of this effort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Artificial neural network is built for the purpose of classification by using the back- propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classification. In the pre-processing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The back-propagation learning algorithm is employed in the multi-layer neural network classifier. The results show that hand contour method has a performance of 71.30% recognition, while complex moments has a better performance of 86.90% recognition rate. 2014 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/4744/1/Thesis_%2D_Haitham.pdf Hasan, Haitham Sabah (2014) Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan. PhD thesis, University of Malaya. http://studentsrepo.um.edu.my/4744/ |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Hasan, Haitham Sabah Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan |
| title | Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
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| title_full | Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
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| title_fullStr | Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
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| title_full_unstemmed | Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
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| title_short | Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
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| title_sort | static hand gesture recognition using artificial neural network / haitham sabah hasan |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://studentsrepo.um.edu.my/4744/ http://studentsrepo.um.edu.my/4744/1/Thesis_%2D_Haitham.pdf |