A novel finger and hand pose estimation technique for real-time hand gesture recognition. (January 2016)

• Record Type: Journal Article
• Authors: Zhou, Yimin; Jiang, Guolai; Lin, Yaorong
• Is Part Of: Pattern recognition. Volume 49(2016:Jan.)
• Page Start: 102
• Page End: 114
• Publication Date: 2016-01
• Subjects: Computer vision -- Finger modelling -- Salient hand edge -- Convolution operator -- Real-time hand gesture recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.07.014
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper presents a high-level hand feature extraction method for real-time gesture recognition. Firstly, the fingers are modelled as cylindrical objects due to their parallel edge feature. Then a novel algorithm is proposed to directly extract fingers from salient hand edges. Considering the hand geometrical characteristics, the hand posture is segmented and described based on the finger positions, palm center location and wrist position. A weighted radial projection algorithm with the origin at the wrist position is applied to localize each finger. The developed system can not only extract extensional fingers but also flexional fingers with high accuracy. Furthermore, hand rotation and finger angle variation have no effect on the algorithm performance. The orientation of the gesture can be calculated without the aid of arm direction and it would not be disturbed by the bare arm area. Experiments have been performed to demonstrate that the proposed method can directly extract high-level hand feature and estimate hand poses in real-time. Abstract : Highlights: A high-level hand feature extraction method for real-time gesture recognition. A novel algorithm is proposed to directly extract fingers from salient hand edges. A weighted radial projection algorithm is applied to locate each finger. The system can not only extract extensional fingers but also flexional fingers with high accuracy. It is robust to the hand rotation, finger side movement and disturbance of the arm area.