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Skeleton-Based Dynamic Hand Gesture Recognition

Abstract : In this paper, a new skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we exploit the geometric shape of the hand to extract an effective de-scriptor from hand skeleton connected joints returned by the Intel RealSense depth camera. Each descriptor is then encoded by a Fisher Vector representation obtained using a Gaussian Mixture Model. A multi-level representation of Fisher Vectors and other skeleton-based geometric features is guaranteed by a temporal pyramid to obtain the final feature vector, used later to achieve the classification by a linear SVM classifier. The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two different numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach.
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Contributor : Hazem Wannous Connect in order to contact the contributor
Submitted on : Thursday, June 8, 2017 - 6:20:13 PM
Last modification on : Wednesday, September 7, 2022 - 8:14:05 AM
Long-term archiving on: : Saturday, September 9, 2017 - 1:43:14 PM


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Quentin de Smedt, Hazem Wannous, Jean-Philippe Vandeborre. Skeleton-Based Dynamic Hand Gesture Recognition. Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on, Jun 2016, Las Vegas, United States. pp.1206 - 1214, ⟨10.1109/CVPRW.2016.153⟩. ⟨hal-01535152⟩



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