scholarly journals Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks

2018 ◽  
pp. 1096-1113 ◽  
Author(s):  
Jiajun Zhang ◽  
Jinkun Tao ◽  
Zhiguo Shi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Doppler Radar ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture

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>

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