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Communication Dans Un Congrès Année : 2016

Skeleton-Based Dynamic Hand Gesture Recognition

Résumé

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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Dates et versions

hal-01535152 , version 1 (08-06-2017)

Identifiants

Citer

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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