scholarly journals A Deep Convolutional Neural Network Approach for Static Hand Gesture Recognition

2020 ◽  
Vol 171 ◽  
pp. 2353-2361
Author(s):  
Adithya V. ◽  
Rajesh R.
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Deep Convolutional Neural Network ◽  
Hand Gesture ◽  
Network Approach ◽  
Neural Network Approach

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.

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