Hand-Gesture Recognition
31 papers with code • 2 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Hand-Gesture Recognition
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Datasets
IPN Hand
VIVA
MLGESTURE DATASET
Human Palm and Gloves Dataset | Human Body Parts Dataset
Most implemented papers
MMTM: Multimodal Transfer Module for CNN Fusion
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end.
IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition
The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR.
Low-latency hand gesture recognition with a low resolution thermal imager
EAVISE/hand-gesture-recognition
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Using hand gestures to answer a call or to control the radio while driving a car, is nowadays an established feature in more expensive cars.
TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars
Furthermore, the gesture recognition classifier has been implemented on a Parallel Ultra-Low Power Processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 100 mW is achieved.
Force myography benchmark data for hand gesture recognition and transfer learning
We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research.
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.
A Deep Learning-based Multimodal Depth-Aware Dynamic Hand Gesture Recognition System
ID56/Multimodal-Fusion-CRNN
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In this paper, we revisit this approach to hand gesture recognition and suggest several improvements.
LIGAR: Lightweight General-purpose Action Recognition
openvinotoolkit/training_extensions
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Furthermore, the induced label noise problem is formulated and Adaptive Clip Selection (ACS) framework is proposed to deal with it.
Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
willogy-team/hand-gesture-recognition-smc2021
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This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH".
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
multimodallearning/hand-gesture-posture-position
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3DV 2022
To induce the global and local stream to capture complementary position and posture features, we propose the use of different 3D learning architectures in both streams.
