Hand Gesture Recognition On Dhg 14

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
e2eET	95.83	Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
DD-Net	94.6	Make Skeleton-based Action Recognition Model Smaller, Faster and Better
TD-GCN	93.9	Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
FPPR-PCD	92.0	Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
DG-STA	91.9	Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
Parallel-Conv	91.28	Deep Learning for Hand Gesture Recognition on Skeletal Data
Ensemble of Models	86.11	An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition
SL-fusion-Average	85.46	CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
SL-fusion-Maximum	85.36	CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
MFANet	84.68	Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition
FL-fusion-Concat	81.86	CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition

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