scholarly journals Retraction Note to: Human–computer interaction using vision-based hand gesture recognition systems: a survey

2017 ◽  
Vol 28 (4) ◽  
pp. 849-849 ◽  
Author(s):  
Haitham Hasan ◽  
Sameem Abdul-Kareem
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Systems ◽  
Computer Interaction

scholarly journals Advances in the Study of Hand Gesture Recognition Systems for Human Computer Interaction

2015 ◽  
Vol 96 (1) ◽  
pp. 19-29
Author(s):  
P. Rodrigo Díaz-Monterrosas ◽  
Rubén Posada-Gómez ◽  
Albino Martínez-Sibaja
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Systems ◽  
Computer Interaction

Recognition of Hand Gesture Image Using Deep Convolutional Neural Network

2021 ◽  
pp. 2140008
Author(s):  
K. Martin Sagayam ◽  
A. Diana Andrushia ◽  
Ahona Ghosh ◽  
Omer Deperlioglu ◽  
Ahmed A. Elngar
Keyword(s):  
Neural Network ◽  
Human Computer Interaction ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
The Body ◽  
Deep Convolutional Neural Network ◽  
Hand Gesture ◽  
Recognition Systems ◽  
Computer Interaction

In recent technology, there is tremendous growth in computer applications that highlight human–computer interaction (HCI), such as augmented reality (AR), and Internet of Things (IoT). As a consequence, hand gesture recognition was highlighted as a very up-to-date research area in computer vision. The body language is a vital method to communicate between people, as well as emphasis on voice messages, or as a complete message on its own. Thus, automatic hand gestures recognition systems can be used to increase human–computer interaction. Therefore, many approaches for hand gesture recognition systems have been designed. However, most of these methods include hybrid processes such as image pre-processing, segmentation, and classification. This paper describes how to create hand gesture model easily and quickly with a well-tuned deep convolutional neural network. Experiments were performed using the Cambridge Hand Gesture data set for illustration of success and efficiency of the convolutional neural network. The accuracy was achieved as 96.66%, where sensitivity and specificity were found to be 85% and 98.12%, respectively, according to the average values obtained at the end of 20 times of operation. These results were compared with the existing works using the same dataset and it was found to have higher values than the hybrid methods.

scholarly journals To Control Electrical MCBs using Hand Gesture Recognition

2015 ◽  
Vol 14 (9) ◽  
pp. 6102-6106
Author(s):  
Sangeeta Goyal ◽  
Dr. Bhupesh Kumar
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Human Body ◽  
Circuit Breaker ◽  
Hand Gesture Recognition ◽  
Hand Movements ◽  
Hand Gesture ◽  
Handicapped Person ◽  
New Techniques ◽  
Computer Interaction

There has been growing interest in development of new techniques and methods for Human-Computer Interaction (HCI). Gesture Recognition is one of the important areas of this technology. Gesture Recognition means interfacing with computer using motion of human body typically hand movements. As a Handicapped person cannot move very easily and quickly if there is a fire in house or he/she cannot switch off the Miniature Circuit Breaker (MCB) but the same task can be done easily with hand gesture recognition. In our proposed system electrical MCB can be controlled using hand gesture recognizer. To switch on/off the MCB, we need to provide hand based gesture as an input to system.

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