Hand Gesture Recognition
33 papers with code • 16 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Hand Gesture Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
SHREC
EgoGesture
NVGesture
Jester (Gesture Recognition)
IPN Hand
VIVA
OREBA
CamGes
Most implemented papers
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
ahmetgunduz/Real-time-GesRec
•
•
We evaluate our architecture on two publicly available datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures.
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed.
HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
Plrbear/HGR-Net
•
•
We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture.
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
dxli94/WLASL
•
•
Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios.
Human Computer Interaction Using Marker Based Hand Gesture Recognition
Human Computer Interaction (HCI) has been redefined in this era.
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Our dataset and experiments can be of interest to communities of 3D hand pose estimation, 6D object pose, and robotics as well as action recognition.
Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition
Gesture recognition is a challenging problem in the field of biometrics.
Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition
Acquiring spatio-temporal states of an action is the most crucial step for action classification.
Deep Learning for Hand Gesture Recognition on Skeletal Data
guillaumephd/deep_learning_hand_gesture_recognition
•
•
IEEE FG 2018 2018
In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training
sbhonde1/Multimodal-Learning
•
•
CVPR 2019
We present an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand gesture recognition.
