Papers › Deep Learning for Hand Gesture Recognition on Skeletal Data

Deep Learning for Hand Gesture Recognition on Skeletal Data

15 May 2018IEEE FG 2018 2018 5archive 2025-07-28

Guillaume Devineau, Wang Xi, Jie Yang, Fabien Moutarde

In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model. We introduce a new Convolutional Neural Network (CNN) where sequences of hand-skeletal joints’ positions are processed by parallel convolutions; we then investigate the performance of this model on hand gesture sequence classification tasks. Our model only uses hand-skeletal data and no depth image. Experimental results show that our approach achieves a state-of-the-art performance on a challenging dataset (DHG dataset from the SHREC 2017 3D Shape Retrieval Contest), when compared to other published approaches. Our model achieves a 91.28% classification accuracy for the 14 gesture classes case and an 84.35% classification accuracy for the 28 gesture classes case.

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Results from the paper archive 2025-07-28

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections