Conférence : International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 4 février 2022
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.