Skip to Main content Skip to Navigation
Conference papers

SPD Siamese Neural Network for Skeleton-based Hand Gesture Recognition

Abstract : This article proposes a new learning method for hand gesture recognition from 3D hand skeleton sequences. We introduce a new deep learning method based on a Siamese network of Symmetric Positive Definite (SPD) matrices. We also propose to use the Contrastive Loss to improve the discriminative power of the network. Experimental results are conducted on the challenging Dynamic Hand Gesture (DHG) dataset. We compared our method to other published approaches on this dataset and we obtained the highest performances with up to 95,60% classification accuracy on 14 gestures and 94.05% on 28 gestures.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03774564
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Sunday, September 11, 2022 - 11:45:31 AM
Last modification on : Tuesday, September 13, 2022 - 3:27:54 AM

Identifiers

Citation

Mohamed Akremi, Rim Slama, Hedi Tabia. SPD Siamese Neural Network for Skeleton-based Hand Gesture Recognition. 17th International Conference on Computer Vision Theory and Applications VISAPP 2022), Feb 2022, Online Streaming, France. pp.394--402, ⟨10.5220/0010822500003124⟩. ⟨hal-03774564⟩

Share

Metrics

Record views

0