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Open Access Skeleton-based Dynamic Hand Gesture Recognition using 3D Depth Data

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Hand gesture recognition is a crucial but challenging task in the field of Virtual Reality (VR) and Human Computer Interaction (HCI). In this paper, a skeleton-based dynamic hand gesture recognition approach is proposed, in which the skeleton structure of the hand captured by 3D depth sensor is firstly exploited and the spatiotemporal multi-fused features that concatenate four skeleton hand shape features and one hand direction feature are extracted. Then the hand shape features are encoded by Fisher Vector obtained from a Gaussian Mixture Model (GMM). To add the temporal information, hand shape Fisher Vector and hand direction feature are represented by a Temporal Pyramid (TP) to obtain the final feature vectors to be fed into a linear SVM classifier to recognize. The proposed approach is evaluated on a challenging dataset containing eight gestures performed by ten participants. Compared with the state-of-the-art dynamic hand gesture recognition methods, the proposed method shows a relative high recognition accuracy of 90.0%.
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Keywords: FISHER VECTOR; GAUSSIAN MIXTURE MODEL; HAND GESTURE RECOGNITION; SKELETON-BASED; SVM

Document Type: Research Article

Publication date: January 28, 2018

This article was made available online on January 13, 2018 as a Fast Track article with title: "Skeleton-based dynamic hand gesture recognition using 3D depth data".

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