Hand Gesture Recognition Based on Computer Vision: A Review of Techniques
TLDR
A review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances, and tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points.Abstract:
Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.read more
Citations
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Journal ArticleDOI
Real-time hand gesture recognition based on deep learning YOLOv3 model
Abdullah Mujahid,Mazhar Javed Awan,Awais Yasin,Mazin Abed Mohammed,Robertas Damaševičius,Rytis Maskeliūnas,Karrar Hameed Abdulkareem +6 more
TL;DR: A lightweight model based on YOLO (You Only Look Once) v3 and DarkNet-53 convolutional neural networks for gesture recognition without additional preprocessing, image filtering, and enhancement of images is proposed.
Journal ArticleDOI
Hand gesture classification using a novel CNN-crow search algorithm
Thippa Reddy Gadekallu,Mamoun Alazab,Rajesh Kaluri,Praveen Kumar Reddy Maddikunta,Sweta Bhattacharya,Kuruva Lakshmanna,M. Parimala +6 more
TL;DR: A crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain and generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.
Journal ArticleDOI
Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities
TL;DR: Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system.
Journal ArticleDOI
Analysis of Edge-Optimized Deep Learning Classifiers for Radar-Based Gesture Recognition
TL;DR: In this paper, a hand gesture recognition-based HCI may serve as a more intuitive mode of human-machine interaction in many situations by simplifying the signal processing pipeline to avoid recurrent structures and efficient topological design.
A new extension of FDOSM based on Pythagorean fuzzy environment for evaluating and benchmarking sign language recognition systems
M. S. Al-Samarraay,Mahmood Maher Salih,M.A. Ahmed,A. A. Zaidan,Osamah Shihab Albahri,Dragan Pamučar,H. A. Alsattar,A.H. Alamoodi,B. B. Zaidan,Kareem Abbas Dawood,Ahmed Shihab Albahri +10 more
TL;DR: This study extended FDOSM into Pythagorean fuzzy set based on the Interactive hybrid arithmetic mean (I HAM) operator (called PFDOSM-IHAM) to evaluate and benchmark effectively the real-time SLRS.
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