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DOI: 10.14569/IJACSA.2023.01403104
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SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data

Author 1: Dayananda Kumar NC
Author 2: K. V Suresh
Author 3: Chandrasekhar V
Author 4: Dinesh R

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

  • Abstract and Keywords
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Abstract: Hand Gesture Recognition (HGR) refers to identifying various hand postures used in Sign Language Recognition (SLR) and Human Computer Interaction (HCI) applications. Complex background in uncontrolled environmental condition is the major challenging issue which impacts the recognition accuracy of HGR system. This can be effectively addressed by discarding the background using suitable semantic segmentation method, where it predicts the hand region pixels into foreground and rest of the pixels into background. In this paper, we have analyzed and evaluated well known semantic segmentation architectures for hand region segmentation using both RGB and depth data. Further, ensemble of segmented RGB and depth stream is used for hand gesture classification through probability score fusion. Experimental results shows that the proposed novel framework of Semantic Segmentation and Ensemble Classification (SSEC) is suitable for static hand gesture recognition and achieved F1-score of 88.91% on OUHANDS test dataset.

Keywords: Hand gesture recognition; semantic segmentation; ensemble classification; score fusion

Dayananda Kumar NC, K. V Suresh, Chandrasekhar V and Dinesh R, “SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01403104

@article{NC2023,
title = {SSEC: Semantic Segmentation and Ensemble Classification Framework for Static Hand Gesture Recognition using RGB-D Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01403104},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01403104},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Dayananda Kumar NC and K. V Suresh and Chandrasekhar V and Dinesh R}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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