Paper Info
Title
Real-Time American Sign Language Recognition Using Skin Segmentation and Image Category Classification with Convolutional Neural Network and Deep Learning
Abstract
A real-time sign language translator is an important milestone in facilitating communication between the deaf community and the general public. We hereby present the development and implementation of an American Sign Language (ASL) fingerspelling translator based on skin segmentation and machine learning algorithms. We present an automatic human skin segmentation algorithm based on color information. The YCbCr color space is employed because it is typically used in video coding and provides an effective use of chrominance information for modeling the human skin color. We model the skin-color distribution as a bivariate normal distribution in the CbCr plane. The performance of the algorithm is illustrated by simulations carried out on images depicting people of different ethnicity. Then Convolutional Neural Network (CNN) is used to extract features from the images and Deep Learning Method is used to train a classifier to recognize Sign Language.
Year
DOI
Venue
2018
10.1109/tencon.2018.8650524
TENCON IEEE Region 10 Conference Proceedings
Keywords
Field
DocType
sign language,skin segmentation,machine learning,YCbCr color space,gesture recognition
Pattern recognition,Computer science,Convolutional neural network,Gesture recognition,Feature extraction,Electronic engineering,Image segmentation,Sign language,American Sign Language,Artificial intelligence,Deep learning,Fingerspelling
Conference
ISSN
Citations 
PageRank 
2159-3442
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Shadman Shahriar100.34
Ashraf Siddiquee200.34
Tanveerul Islam300.68
Abesh Ghosh400.34
Rajat Chakraborty501.01
Asir Intisar Khan602.03
Celia Shahnaz712225.95
Shaikh Anowarul Fattah88222.70