Hand pose recognition from monocular images by geometrical and texture analysis

Bhuyan, M. K., MacDorman, Karl F., Kar, Mithun Kumar, Neog, Debanga Raj, Lovell, Brian C. and Gadde, Prathik (2015) Hand pose recognition from monocular images by geometrical and texture analysis. Journal of Visual Languages and Computing, 28 39-55. doi:10.1016/j.jvlc.2014.12.001

Author Bhuyan, M. K.
MacDorman, Karl F.
Kar, Mithun Kumar
Neog, Debanga Raj
Lovell, Brian C.
Gadde, Prathik
Title Hand pose recognition from monocular images by geometrical and texture analysis
Journal name Journal of Visual Languages and Computing   Check publisher's open access policy
ISSN 1045-926X
Publication date 2015-06-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.jvlc.2014.12.001
Volume 28
Start page 39
End page 55
Total pages 17
Place of publication London, United Kingdom
Publisher Academic Press
Language eng
Abstract One challenging research problem of hand pose recognition is the accurate detection of finger abduction and flexion with a single camera. The detection of flexion movements from a 2D image is difficult, because it involves estimation of finger movements along the optical axis of the camera (z direction). In this paper, a novel approach to hand pose recognition is proposed. We use the concept of object-based video abstraction for segmenting the frames into video object planes (VOPs), as used in MPEG-4, with each VOP corresponding to one semantically meaningful hand position. Subsequently, a particular hand pose is recognized by analyzing the key geometrical features and the textures of the hand. The abduction and adduction movements of the fingers are analyzed by considering a skeletal model. Probabilistic distributions of the geometric features are considered for modeling intra-class abduction and adduction variations. Additionally, gestures differing in flexion positions of the fingers are classified by texture analysis using homogeneous texture descriptors (HTD). Finally, hand poses are classified based on proximity measurement by considering the intra-class abduction and adduction and/or inter-class flexion variations. Experimental results show the efficacy of our proposed hand pose recognition system. The system achieved a 99% recognition rate for one-hand poses and a 97% recognition rate for two-hand poses.
Keyword Hand model
Hand pose recognition
Homogeneous texture descriptors (HTD)
Human-computer interaction (HCI)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 19 Dec 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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