Open AccessHand Gesture Recognition for Emoji and Text Prediction
Author(s) -
Usha Kiruthika,
Mahesh Mohan,
Nixon M. Abraham
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k1220.09811s19
Subject(s) - scale invariant feature transform , computer science , gesture , artificial intelligence , convolutional neural network , filter (signal processing) , computer vision , object (grammar) , rotation (mathematics) , pattern recognition (psychology) , speech recognition , image (mathematics)
Emoticons' are ideograms and smileys utilized in electronic messages and website pages. Emoticons exist in different classifications, including outward appearances, regular items, places and kinds of climate, and creatures. They are much similar to emojis, however emoticons are real pictures rather than typo graphics. This undertaking perceives the emoticons utilizing hand motions. We are detecting hand gestures and preparing a Convolutional Neural Network (CNN) model on a training dataset. We will make a database of hand gestures and train them. The system utilized here is a CNN. We are utilizing the SIFT filter to identify the hand and CNN for preparing the model. SIFT filter give a lot of highlights of an image that are not influenced by numerous factors, for example, object scaling and rotation. The SIFT filtering procedure comprises of two areas. The first is a procedure to identify intrigue focuses in the hand. Intrigue focuses are the points in the image in a 2D space that surpasses some limit measure and is better than straight forward edge recognition. The second segment is a procedure to make a vector like descriptor and this is the most special and prevalent part of the SIFT filter.