Review of constraints on vision-based gesture recognition for human–computer interaction

The ability of computers to recognise hand gestures visually is essential for progress in human–computer interaction. Gesture recognition has applications ranging from sign language to medical assistance to virtual reality. However, gesture recognition is extremely challenging not only because of it...

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Bibliographic Details
Published in:IET computer vision Vol. 12; no. 1; pp. 3 - 15
Main Authors: Chakraborty, Biplab Ketan, Sarma, Debajit, Bhuyan, M.K, MacDorman, Karl F
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 01.02.2018
Wiley
Subjects:
complex nonrigid properties
diverse contexts
feature extraction
feature recognition
gesture recognition
hand gesture recognition
human-computer interaction
man-machine systems
medical assistance
multiple interpretations
sign language
spatio-temporal variations
Special Section: Computer Vision in Healthcare and Assisted Living
virtual reality
vision-based gesture recognition
diverse contexts
virtual reality
spatio-temporal variations
medical assistance
multiple interpretations
vision-based gesture recognition
man-machine systems
sign language
human-computer interaction
gesture recognition
feature extraction
feature recognition
hand gesture recognition
complex nonrigid properties
ISSN:1751-9632, 1751-9640, 1751-9640
Online Access:Get full text
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Summary:The ability of computers to recognise hand gestures visually is essential for progress in human–computer interaction. Gesture recognition has applications ranging from sign language to medical assistance to virtual reality. However, gesture recognition is extremely challenging not only because of its diverse contexts, multiple interpretations, and spatio-temporal variations but also because of the complex non-rigid properties of the hand. This study surveys major constraints on vision-based gesture recognition occurring in detection and pre-processing, representation and feature extraction, and recognition. Current challenges are explored in detail.
ISSN:1751-9632
1751-9640
1751-9640
DOI:10.1049/iet-cvi.2017.0052