Open AccessFacial Expression Recognition Using KERAS
Author(s) -
Syed Inthiyaz,
Mohd Parvez,
M. Siva Kumar,
J. Sri sai Srija,
M. Tarun Sai,
V. Amruth Vardhan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1804/1/012202
Subject(s) - deep learning , artificial intelligence , computer science , convolutional neural network , disgust , sadness , surprise , facial expression , machine learning , face (sociological concept) , set (abstract data type) , pattern recognition (psychology) , speech recognition , anger , psychology , social psychology , social science , psychiatry , sociology , programming language
Recognition of Facial expression in technology plays a major role in many sectors. It has many advantages because of which it is very important. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural networks (CNN). The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality. The main objective here in this project is, to read the facial expressions of the people and displaying them. OpenCV is used for automatic detection of faces and drawing bounding boxes around them. Face detection using the Hear cascades is a machine learning based algorithm where a cascade function will be trained with a set of input data. OpenCV contains many pre-trained classifiers for face, eyes, smile etc. The deep learning is a subset of machine learning. Deep learning is used by Google to translate the information form one language to another using deep learning approach. The network should be trained with relatively more data in deep learning.