An overview of hand gesture recognition based on computer vision

Tasfia, Rifa and Mohd Yusoh, Zeratul Izzah and Habib, Adria Binte and Mohaimen, Tousif (2024) An overview of hand gesture recognition based on computer vision. International Journal of Electrical and Computer Engineering, 14 (4). pp. 4636-4645. ISSN 2088-8708

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Abstract

Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the domain of vision-based hand gesture recognition. The objective is to find out the most prominent methods by reviewing efficiency. Concurrently, the paper delves into presenting potential solutions for challenges faced in different research. A comparative analysis particularly centered around traditional methods and convolutional neural networks like random forest, long short-term memory (LSTM), heatmap, and you only look once (YOLO). considering their efficacy. Where convolutional neural network-based algorithms performed best for recognizing the gestures and gave effective solutions for the challenges faced by the researchers. In essence, the findings of this review paper aim to contribute to future implementations and the discovery of more efficient approaches in the gesture recognition sector.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, Convolutional neural network, Deep neural network, Difficulties in hand gesture recognition, Hand gesture recognition, Hand gesture recognition methodology
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	04 Oct 2024 17:00
Last Modified:	04 Oct 2024 17:00
URI:	http://eprints.utem.edu.my/id/eprint/27687
Statistic Details:	View Download Statistic

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