A review of the hand gesture recognition system: Current progress and future directions

Mohamed, Noraini and Mustafa, Mumtaz Begum and Jomhari, Nazean (2021) A review of the hand gesture recognition system: Current progress and future directions. IEEE Access, 9. pp. 157422-157436. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3129650.

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This paper reviewed the sign language research in the vision-based hand gesture recognition system from 2014 to 2020. Its objective is to identify the progress and what needs more attention. We have extracted a total of 98 articles from well-known online databases using selected keywords. The review shows that the vision-based hand gesture recognition research is an active field of research, with many studies conducted, resulting in dozens of articles published annually in journals and conference proceedings. Most of the articles focus on three critical aspects of the vision-based hand gesture recognition system, namely: data acquisition, data environment, and hand gesture representation. We have also reviewed the performance of the vision-based hand gesture recognition system in terms of recognition accuracy. For the signer dependent, the recognition accuracy ranges from 69% to 98%, with an average of 88.8% among the selected studies. On the other hand, the signer independent's recognition accuracy reported in the selected studies ranges from 48% to 97%, with an average recognition accuracy of 78.2%. The lack in the progress of continuous gesture recognition could indicate that more work is needed towards a practical vision-based gesture recognition system.

Item Type: Article
Funders: University Malaya Research Grant (UMRG) [RG284-14AFR], Fundamental Research Grant Scheme (FRGS) [FP062-2020]
Uncontrolled Keywords: Gesture recognition; Assistive technologies; Data acquisition; Databases; Libraries; Human computer interaction; Object recognition; Classification; feature extraction; dynamic hand gesture recognition; sign language recognition; vision-based hand gesture; recognition accuracy
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology > Department of Software Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 11 May 2022 08:27
Last Modified: 11 May 2022 08:27
URI: http://eprints.um.edu.my/id/eprint/27113

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