 |
 |
Conference papers
Deep Learning for Hand Gesture Recognition on Skeletal Data
Abstract : — 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.
Cited literature [47 references]
https://hal-mines-paristech.archives-ouvertes.fr/hal-01737771
Contributor : Fabien Moutarde <>
Submitted on : Thursday, March 22, 2018 - 4:02:17 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Tuesday, September 11, 2018 - 8:24:42 AM
Contributor : Fabien Moutarde <>
Submitted on : Thursday, March 22, 2018 - 4:02:17 PM
Last modification on : Thursday, September 24, 2020 - 5:04:02 PM
Long-term archiving on: : Tuesday, September 11, 2018 - 8:24:42 AM
File
DeepLearning-HandSkeletalGestu...
Files produced by the author(s)