In-air signature is a behavioral biometric trait that has gained increasing attention in recent years due to its contactless nature and potential for secure, hygienic, and remote authentication. Unlike traditional pen-and-paper or tablet-based system...
Human activity recognition (HAR) has numerous applications due to its widespread use of procurement tools, such as smartphones and video cameras, and its ability to capture data on human activity. HAR became a hot scientific area in the computer visi...
This paper introduces the Gray Wolf Optimized Convolutional Transformer Network, a combined deep learning framework aimed at accurately and efficiently recognizing dynamic hand gestures, especially in American Sign Language (ASL). The model integrate...
Generally, the interaction of gestures presents a set of benefits to persons with disabilities, from improving motor, social, and cognitive skills to delivering a secure and controlled atmosphere for engaging in real-world scenarios. Automatic detect...
Real-time and accurate hand gesture detection is essential for safe and intuitive Human-Robot Interaction (HRI), enabling robots to interpret non-verbal cues and respond appropriately in dynamic environments. This research evaluates the effectiveness...
Saudi Arabic Sign Language (SArSL) recognition poses significant challenges due to its complex spatio-temporal structure and the scarcity of annotated datasets. This paper introduces a self-supervised learning framework built upon the Video Momentum ...
Gesture recognition (GR) is an emerging and wide-ranging area of research. GR is extensively applied in sign language, Immersive game technology, and other computer interfaces, among others. People with visual impairments face challenges in completin...
In this paper, we propose a novel neural network architecture, the convolutional spider neural network (CS-Net), combined with a transfer learning (TL) strategy, to classify hybrid gestures that integrate wrist postures and hand movements.The CS-Net ...
Automated behavioral measurement using machine learning is gaining ground in psychological research. Automated approaches have the potential to reduce the labor and time associated with manual behavioral coding, and to enhance measurement objectivity...
In recent years, Hand Gesture Recognition (HGR) devices have been designed to recognize gestures in real time using machine-learning classifiers (MLCs). However, the performance of these classifiers heavily relies on the tuning of their hyperparamete...
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