scholarly journals A new hand gestures recognition system

2020 ◽  
Vol 18 (1) ◽  
pp. 49
Author(s):  
Ahmed Kadem Hamed AlSaedi ◽  
Abbas H. Hassin AlAsadi
Keyword(s):  
Low Cost ◽  
Computer Control ◽  
Recognition System ◽  
Hand Gesture ◽  
Human Communication ◽  
Hand Gestures ◽  
Cost System ◽  
Python Language ◽  
Hand Region ◽  
Hand Gestures Recognition

<p>Talking about gestures makes us return to the historical beginning of human communication, because, in fact, there is no language completely free of gestures. People cannot communicate without gestures. Any action or movement without gestures is free of real feelings and cannot express the thoughts. The purpose from any hand gestures recognition system is to recognizes the hand gesture and used it to transfer a certain meaning or for computer control or and device. Our paper introduced a low cost system to recognize the hand gesture in real-time. Generally, the system divided into five steps, one to image acquisition, second to pre-processing the image, third for detection and segmentation of hand region, four to features extraction and five to count the numbers of fingers and gestures recognition. The system has coded by Python language, PyAutoGUI library, OS Module of Python and the OpenCV library.</p>

scholarly journals Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Peng Liu ◽  
Xiangxiang Li ◽  
Haiting Cui ◽  
Shanshan Li ◽  
Yafei Yuan
Keyword(s):  
Deep Learning ◽  
Gesture Recognition ◽  
Learning Algorithm ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Single Shot ◽  
Complex Scene ◽  
Hand Gestures ◽  
Benchmark Database

Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.

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>

STATIC HAND GESTURES RECOGNITION SYSTEM WITH USING DEPTH CAMERA

2020 ◽  
Vol 72 ◽  
pp. 93-105
Author(s):  
D. Zh. Satybaldina ◽  
◽  
G. V. Ovechkin ◽  
G. A. Kalymova ◽  
◽  
...  
Keyword(s):  
Recognition System ◽  
Depth Camera ◽  
Hand Gestures ◽  
Hand Gestures Recognition

HUMAN-COMPUTER INTERACTION USING DYNAMIC HAND GESTURE RECOGNITION TO CONVENIENTLY CONTROL THE SYSTEM

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Smit Parikh ◽  
Srikar Banka ◽  
Isha Lautrey ◽  
Isha Gupta ◽  
Prof Dhanalekshmi Yedurkar
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Basic Functions ◽  
Dynamic Hand Gesture Recognition ◽  
Basic Features ◽  
Computer Interaction

The use of a physical controller such as a mouse, a keyboard for human computer interaction hinders the natural interface since the user and computer have a high barrier. Our aim is to create an application that controls some basic features of computers using hand gestures through an integrated webcam to resolve this issue. A Hand Gesture Recognition system detects gestures and translates them into specific actions to make our work easier. This can be pursued using OpenCV to capture the gestures which will be interfaced using Django, React.Js and Electron. An algorithm named YOLO is used to train the system accordingly. The gestures will get saved inside the DBMS. The main result expected is that the user will be able to control the basic functions of the system using his/her hand gestures and hence providing them utmost comfort.

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