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Hand Gesture Recognition On Dhg 14

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
Parallel-Conv91.28Deep Learning for Hand Gesture Recognition on Skeletal Data
MFANet84.68Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition-
DD-Net94.6Make Skeleton-based Action Recognition Model Smaller, Faster and Better
SL-fusion-Maximum85.36CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition-
TD-GCN93.9Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition-
e2eET95.83Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
SL-fusion-Average85.46CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition-
FPPR-PCD92.0Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
DG-STA91.9Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
FL-fusion-Concat81.86CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition-
Ensemble of Models86.11An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition-
0 of 11 row(s) selected.