SPD Siamese Neural Network for skeleton-based hand gesture recognition
Conférence : Communications avec actes dans un congrès international
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.