Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emojis in Computer-Mediated Communication

2019 ◽  
Vol 9 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Jung In Koh ◽  
Josh Cherian ◽  
Paul Taele ◽  
Tracy Hammond
Keyword(s):  
Gesture Recognition ◽  
Computer Mediated Communication ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Mediated Communication ◽  
Hand Gestures ◽  
Computer Mediated

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.

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.

scholarly journals Analisis Penerapan Metode Convex Hull Dan Convexity Defects Untuk Pengenalan Isyarat Tangan

2018 ◽  
Vol 8 (2) ◽  
pp. 105
Author(s):  
Artha Gilang Saputra ◽  
Ema Utami ◽  
Hanif Al Fatta
Keyword(s):  
Convex Hull ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Input Device ◽  
Human Beings ◽  
Light Conditions ◽  
Hand Gestures ◽  
Voice Communication

Research of Human Computer Interaction (HCI) and Computer Vision (CV) is increasingly focused on advanced interface for interacting with humans and creating system models for various purposes. Especially for input device problem to interact with computer. Humans are accustomed to communicate with fellow human beings using voice communication and accompanied by body pose and hand gesture. The main purpose of this research is to applying the Convex Hull and Convexity Defects methods for Hand Gesture Recognition system. In this research, the Hand Gesture Recognition system designed with the OpenCV library and then receives input from the user's hand gesture using an integrated webcam on the computer and system generates a language output from the hand-recognizable gestures. Testing involves several variables which affect success in recognizing user's hand gestures, such as hand distance with webcam, corner of the finger, light conditions and background conditions. As a result, the user's hand gestures can be recognized with a stable and accurate when at a distance of 50cm-70cm, corner of the finger 25o–70o, light conditions 150lux-460lux and plain background conditions.

scholarly journals Authenticating user while Performing Transaction at ATM/POS using Hand Gestures

2021 ◽  
Vol 7 (01) ◽  
pp. 23-26
Author(s):  
Rahul Gupta, Keshav Choudhary, Sagar Kashyap Vishisht Chhabra and Jyoti Verma
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Position ◽  
Electronic Document ◽  
Hand Gestures ◽  
Finger Position

This electronic document defines the use of Vision-based technology for authentication at ATM/POS while performing a transaction. It makes use of Hand Gestures which are conveyed through finger position and shape of a palm, which helps in entering the ATM pin through gestures of the hand. The hand gestures are being detected using OpenCV, python 3.7. A High-uality camera and well-lit background are required for proper detection of hand position. This Paper will propose to develop a hand gesture recognition system for safely performing a transaction at ATM/POS.

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

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