Hand pose recognition from monocular images by geometrical and texture analysis. (June 2015)

• Record Type: Journal Article
• Authors: Bhuyan, M.K.; MacDorman, Karl F.; Kar, Mithun Kumar; Neog, Debanga Raj; Lovell, Brian C.; Gadde, Prathik
• Is Part Of: Journal of visual languages & computing. Volume 28(2015)
• Page Start: 39
• Page End: 55
• Publication Date: 2015-06
• Subjects: Hand model -- Hand pose recognition -- Homogeneous texture descriptors (HTD) -- Human–computer interaction (HCI); Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2014.12.001
• Languages: English
• ISSNs: 1045-926X

Abstract

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. Abstract : Highlights: Proposed a novel scheme for hand pose recognition for HCI. Proposed an object-based video abstraction method for hand segmentation. Abduction angle variations are modeled by geometrical features. Flexion angle variations are modeled by analyzing textures of the fingers. Achieved 99% and 97% recognition rate for one-hand and two-hand poses respectively.