Hand Gesture Recognition
41 papers with code • 18 benchmarks • 14 datasets
Hand gesture recognition (HGR) is a subarea of Computer Vision where the focus is on classifying a video or image containing a dynamic or static, respectively, hand gesture. In the static case, gestures are also generally called poses. HGR can also be performed with point cloud or joint hand data.
Benchmarks
These leaderboards are used to track progress in Hand Gesture Recognition
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Datasets
EgoGesture
SHREC
NVGesture
Jester (Gesture Recognition)
IPN Hand
VIVA
RWTH-PHOENIX Handshapes dev set
OREBA
LSA16
Latest papers with no code
It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera
Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature.
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition
In this paper, we propose the design of a Vision Transformer (ViT) based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform Hand Gesture Recognition from surface electromyography (sEMG) signals.
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as surface electromyography (sEMG).
Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition
In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy.
Hand Gesture Recognition with Two Stage Approach Using Transfer Learning and Deep Ensemble Learning
By utilizing these models as base learners and applying the Dirichlet ensemble technique, we achieved an accuracy rate of 98. 88%.
On-device Real-time Custom Hand Gesture Recognition
Our framework provides a pre-trained single-hand embedding model that can be fine-tuned for custom gesture recognition.
From Unimodal to Multimodal: improving sEMG-Based Pattern Recognition via deep generative models
Conclusion: It demonstrates that incorporating virtual IMU signals, generated by deep generative models, can significantly improve the accuracy of sEMG-based HGR.
sEMG-based Hand Gesture Recognition with Deep Learning
Two-posture training proves the best postural training (proving the benefit of training on more than one posture) and yields 81. 2% inter-posture test accuracy.
CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control
We present CaptAinGlove, a textile-based, low-power (1. 15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control.
Agile gesture recognition for capacitive sensing devices: adapting on-the-job
However, there is growing demand for gesture recognition technology that can be implemented on low-power devices using limited sensor data instead of high-dimensional inputs like hand images.
