A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network
Gesture recognition has gained a lot of popularity as it allows humans to communicate with real or virtual systems through gestures, offering new and natural interaction modalities. Recent technologies, such as augmented reality (AR) and the Internet of Things (IoT), have witnessed enormous growth i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-10-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2251229 |
| _version_ | 1797245044111966208 |
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| author | K. Harini S. Uma Maheswari |
| author_facet | K. Harini S. Uma Maheswari |
| author_sort | K. Harini |
| collection | DOAJ |
| description | Gesture recognition has gained a lot of popularity as it allows humans to communicate with real or virtual systems through gestures, offering new and natural interaction modalities. Recent technologies, such as augmented reality (AR) and the Internet of Things (IoT), have witnessed enormous growth in computer applications that focus on human–computer interaction (HCI). However, a few of these tactics make use of a combination of methods, such as image segmentation, pre-processing, and classification. The hessian-based multiscale filtering and YCbCr colour space are used to separate the gesture region to be recognized. A modified marker-controlled watershed method is employed to segment the gesture contour along with the eight-connector graph to increase recognition precision. The proposed hand gesture recognition methodology uses Self Organizing Map (SOM) with Deep Convolutional Neural Network (DCNN) provides better results with fast convergence speed. Experiments were carried out on a dataset of 30 static and 6 dynamic gestures and also evaluated on a publicly available IIITA-ROBITA ISL Gesture Database to show the effectiveness. The results show that the suggested method can recognize gesture classes with 95.63% accuracy rate without significantly affecting the recognition time. The proposed algorithm was then implemented to control household appliances. |
| first_indexed | 2024-03-10T15:57:45Z |
| format | Article |
| id | doaj.art-4e66c9e3528d41f496db1cebef236590 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-24T19:20:38Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-4e66c9e3528d41f496db1cebef2365902024-03-25T18:18:03ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-10-016441128114010.1080/00051144.2023.2251229A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural networkK. Harini0S. Uma Maheswari1Department of ECE, Coimbatore Institute of Technology, Coimbatore, IndiaDepartment of ECE, Coimbatore Institute of Technology, Coimbatore, IndiaGesture recognition has gained a lot of popularity as it allows humans to communicate with real or virtual systems through gestures, offering new and natural interaction modalities. Recent technologies, such as augmented reality (AR) and the Internet of Things (IoT), have witnessed enormous growth in computer applications that focus on human–computer interaction (HCI). However, a few of these tactics make use of a combination of methods, such as image segmentation, pre-processing, and classification. The hessian-based multiscale filtering and YCbCr colour space are used to separate the gesture region to be recognized. A modified marker-controlled watershed method is employed to segment the gesture contour along with the eight-connector graph to increase recognition precision. The proposed hand gesture recognition methodology uses Self Organizing Map (SOM) with Deep Convolutional Neural Network (DCNN) provides better results with fast convergence speed. Experiments were carried out on a dataset of 30 static and 6 dynamic gestures and also evaluated on a publicly available IIITA-ROBITA ISL Gesture Database to show the effectiveness. The results show that the suggested method can recognize gesture classes with 95.63% accuracy rate without significantly affecting the recognition time. The proposed algorithm was then implemented to control household appliances.https://www.tandfonline.com/doi/10.1080/00051144.2023.2251229hand gesture recognitionhuman–computer interactionhessian-based multiscale filteringmodified marker-controlled watershed algorithmeight-connected filling algorithmSOM-based deep convolutional neural network |
| spellingShingle | K. Harini S. Uma Maheswari A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network Automatika hand gesture recognition human–computer interaction hessian-based multiscale filtering modified marker-controlled watershed algorithm eight-connected filling algorithm SOM-based deep convolutional neural network |
| title | A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network |
| title_full | A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network |
| title_fullStr | A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network |
| title_full_unstemmed | A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network |
| title_short | A novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network |
| title_sort | novel static and dynamic hand gesture recognition using self organizing map with deep convolutional neural network |
| topic | hand gesture recognition human–computer interaction hessian-based multiscale filtering modified marker-controlled watershed algorithm eight-connected filling algorithm SOM-based deep convolutional neural network |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2251229 |
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