scholarly journals Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

2021 ◽  
Author(s):  
Arpita Vats
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Camshift Algorithm ◽  
Hand Region

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

scholarly journals Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

2021 ◽  
Author(s):  
Arpita Vats
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Camshift Algorithm ◽  
Hand Region

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

A Model for Real-Time Hand Gesture Recognition Using Electromyography (EMG), Covariances and Feed-Forward Artificial Neural Networks

2020 ◽  
Author(s):  
Marco E. Benalcazar ◽  
Jose Gonzalez ◽  
Andres Jaramillo-Yanez ◽  
Carlos E. Anchundia ◽  
Patricio Zambrano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Feed Forward ◽  
Artificial Neural

scholarly journals FPGA based Real Time Human Hand Gesture Recognition System

2012 ◽  
Vol 6 ◽  
pp. 98-107 ◽  
Author(s):  
Amit Gupta ◽  
Vijay Kumar Sehrawat ◽  
Mamta Khosla
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand

scholarly journals Real-time Hand-Gesture Recognition based on Deep Neural Network

2021 ◽  
Vol 102 ◽  
pp. 04009
Author(s):  
Naoto Ageishi ◽  
Fukuchi Tomohide ◽  
Abderazek Ben Abdallah
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Gesture Recognition ◽  
Deep Neural Network ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Robotic Control ◽  
Driver Assistance Systems ◽  
Fast Classification

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.

A real-time hand gesture recognition system for daily information retrieval from the internet

2015 ◽  
Vol 11 (2) ◽  
pp. 105 ◽  
Author(s):  
Joseph C. Tsai ◽  
Shih Ming Chang ◽  
Shwu Huey Yen ◽  
Kuan Ching Li ◽  
Yung Hui Chen ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
The Internet

A Real Time Hand Gesture Recognition System Based on the Prior Facial Knowledge and SVM

2013 ◽  
Vol 8 (11) ◽  
pp. 185-193 ◽  
Author(s):  
Jiali Li ◽  
Lingxiang Zheng ◽  
Yuqi Chen ◽  
Yixiong Zhang ◽  
Peng Lu
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture
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