Open AccessClassifying Facial Expression using Convolution Neural Network
Author(s) -
Khamael Raqim Raheem,
Israa Hadi Ali
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/928/3/032036
Subject(s) - facial expression , convolutional neural network , computer science , artificial intelligence , expression (computer science) , pattern recognition (psychology) , convolution (computer science) , focus (optics) , task (project management) , face (sociological concept) , facial expression recognition , speech recognition , function (biology) , artificial neural network , facial recognition system , engineering , social science , physics , systems engineering , sociology , optics , programming language , evolutionary biology , biology
The human-computer interaction system is a success by deriving an effective facial expression recognition function. But it remains a difficult activity to understand facial speech. This paper sets out a novel Recognition of facial expression approach to the task. The approach proposed is motivated by the performance of the Convolutional Neural Networks (CNN) on the face trouble with identification. Unlike other plays, we focus on having good accuracy while requiring only a small sample data for training. The proposed approach is tested on Japanese Female Facial Expression (JAFFE). The accuracy increased compared with state-of-art results on the JAFEE dataset, where it achieved 95%.