SPD Siamese Neural Network for Skeleton-based Hand Gesture Recognition

被引：4
|
作者：
Akremi, Mohamed Sanim [1 ]
Slama, Rim [2 ]
Tabia, Hedi [1 ]
机构：
[1] Paris Saclay Univ, Univ Evry, IBISC, Evry, France
[2] CESI Lyon, LINEACT Lab, Lyon, France
来源：
PROCEEDINGS OF THE 17TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISAPP), VOL 4 | 2022年
关键词：
SPD Learning Model; Siamese Network; Deep Learning; Hand Gesture Recognition; Skeletal Data;
D O I：
10.5220/0010822500003124
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：394 / 402
页数：9
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