Hand Gesture Recognition Based on Computer Vision: A Review of Techniques.

computer vision hand gesture hand posture human–computer interaction (HCI)

Journal

Journal of imaging
ISSN: 2313-433X
Titre abrégé: J Imaging
Pays: Switzerland
ID NLM: 101698819

Informations de publication

Date de publication:
23 Jul 2020
Historique:
received: 23 05 2020
revised: 15 07 2020
accepted: 21 07 2020
entrez: 30 8 2021
pubmed: 31 8 2021
medline: 31 8 2021
Statut: epublish

Résumé

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human-computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

Identifiants

pubmed: 34460688
pii: jimaging6080073
doi: 10.3390/jimaging6080073
pmc: PMC8321080
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Auteurs

Munir Oudah (M)

Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, Iraq.

Ali Al-Naji (A)

Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, Iraq.
School of Engineering, University of South Australia, Mawson Lakes SA 5095, Australia.

Javaan Chahl (J)

School of Engineering, University of South Australia, Mawson Lakes SA 5095, Australia.

Classifications MeSH