scholarly journals Face and Hand Gesture Recognition System for Controlling VLC Media Player

2021 ◽  
pp. 119-124
Author(s):  
K M Bilvika ◽  
Sneha B K ◽  
Sahana K M ◽  
Tejaswini S M Patil
Keyword(s):  
Computer Vision ◽  
Sign Language ◽  
Gesture Recognition ◽  
Vision Research ◽  
Recognition System ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Media Player ◽  
Language Interpretation ◽  
Intermediate Medium

In human-computer interaction or sign language interpretation, recognizing hand gestures and face detection become predominant in computer vision research. The primary goal of this proposed system is to create a system, which can identify hand gestures and facial detection to convey information for controlling media player. For those who are deaf and dumb sign language is a common, efficient and alternative way for talking, by using the hand and facial gestures we can easily understand them. Here hand and face are directly use as the input to the device for effective communication purpose of gesture identification there is no need of an intermediate medium.

scholarly journals Computer Vision Based Hand Gesture Recognition For Speech Disabled Persons

2016 ◽  
Vol 11 (1) ◽  
pp. 30-35
Author(s):  
Manoj Acharya ◽  
Dibakar Raj Pant
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sign Language ◽  
Gesture Recognition ◽  
Disabled Persons ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Dependency Analysis ◽  
Hand Gestures

This paper proposes a method to recognize static hand gestures in an image or video where a person is performing Nepali Sign Language (NSL) and translate it to words and sentences. The classification is carried out using Neural Network where contour of the hand is used as the feature. The work is verified successfully for NSL recognition using signer dependency analysis. Journal of the Institute of Engineering, 2015, 11(1): 30-35

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 HAND GESTURE RECOGNITION FOR INDONESIAN SIGN LANGUAGE INTERPRETER SYSTEM WITH MYO ARMBAND USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 7 (2) ◽  
pp. 164
Author(s):  
Aditiya Anwar ◽  
Achmad Basuki ◽  
Riyanto Sigit
Keyword(s):  
Support Vector Machine ◽  
Sign Language ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Deaf People ◽  
Hand Gestures ◽  
Moment Invariant ◽  
Myo Armband

<p><em>Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning.  In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. </em><em>This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant</em></p>

scholarly journals Development of Hand Gesture Based Electronic Key Using Microsoft Kinect

2018 ◽  
Vol 218 ◽  
pp. 02014
Author(s):  
Arief Ramadhani ◽  
Achmad Rizal ◽  
Erwin Susanto
Keyword(s):  
Computer Vision ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Hand Gesture ◽  
Depth Sensor ◽  
Average Accuracy ◽  
Recognition Result ◽  
Hand Image

Computer vision is one of the fields of research that can be applied in a various subject. One application of computer vision is the hand gesture recognition system. The hand gesture is one of the ways to interact with computers or machines. In this study, hand gesture recognition was used as a password for electronic key systems. The hand gesture recognition in this study utilized the depth sensor in Microsoft Kinect Xbox 360. Depth sensor captured the hand image and segmented using a threshold. By scanning each pixel, we detected the thumb and the number of other fingers that open. The hand gesture recognition result was used as a password to unlock the electronic key. This system could recognize nine types of hand gesture represent number 1, 2, 3, 4, 5, 6, 7, 8, and 9. The average accuracy of the hand gesture recognition system was 97.78% for one single hand sign and 86.5% as password of three hand signs.

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