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Nadia Moqbel Hassan
Mustansiraih University, Iraq
Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Convolutional neural networks framework for human hand gesture recognition Aseel Ghazi Mahmoud; Ahmed Mudheher Hasan; Nadia Moqbel Hassan
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2926

Abstract

Recently, the recognition of human hand gestures is becoming a valuable technology for various applications like sign language recognition, virtual games and robotics control, video surveillance, and home automation. Owing to the recent development of deep learning and its excellent performance, deep learning-based hand gesture recognition systems can provide promising results. However, accurate recognition of hand gestures remains a substantial challenge that faces most of the recently existing recognition systems. In this paper, convolutional neural networks (CNN) framework with multiple layers for accurate, effective, and less complex human hand gesture recognition has been proposed. Since the images of the infrared hand gestures can provide accurate gesture information through the low illumination environment, the proposed system is tested and evaluated on a database of hand-based near-infrared which including ten gesture poses. Extensive experiments prove that the proposed system provides excellent results of accuracy, precision, sensitivity (recall), and F1-score. Furthermore, a comparison with recently existing systems is reported.
Co-Authors Ahmed Mudheher Hasan Aseel Ghazi Mahmoud