An image-to-class dynamic time warping approach for both 3D static and trajectory hand gesture recognition. (July 2016)

• Record Type: Journal Article
• Authors: Cheng, Hong; Dai, Zhongjun; Liu, Zicheng; Zhao, Yang
• Is Part Of: Pattern recognition. Volume 55(2016:Jul.)
• Page Start: 137
• Page End: 147
• Publication Date: 2016-07
• Subjects: Image-to-class distance -- Fingerlets -- Strokelets -- Dynamic time warping -- 3D hand gesture recognition -- Human computer interaction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.01.011
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: In this paper, we present an Image-to-Class Dynamic Time Warping (I2C-DTW) approach for the recognition of both 3D static hand gestures and 3D hand trajectory gestures. Our contribution is twofold. First, we propose a technique to compute the image-to-class dynamic time warping distance instead of the Image-to-Image distance. By doing so, we obtain better generalization capability using the Image-to-Class distance than the Image-to-Image distance. Second, we propose a compositional model called fingerlets for static gesture representation, and a compositional model called strokelets for trajectory gesture representation. The compositional models make it possible to compute the DTW distance between a data sample and a gesture category. We have evaluated the static gesture recognition performance on several public 3D hand gesture datasets. For better evaluating the performance on trajectory gesture recognition, we collected a 3D hand trajectory gesture dataset, called UESTC-HTG, using a Kinect device. The experiment results show that the proposed I2C-DTW approach significantly improves the recognition accuracy on both static gestures and trajectory gestures. Abstract : Highlights: A technique to compute the Image-to-Class DTW distance is proposed. A fingerlets' model for static gesture representation is proposed. A strokelets' model for trajectory gesture representation is proposed. A 3D hand trajectory gesture dataset (UESTC-HTG) is built by Kinect.