Open AccessFacial Expression Analysis using Convolutional Neural Networks
Author(s) -
Amogh S. Gopadi,
Shashikant Deepak,
Ravi Kiran,
A M Naveena,
M S Srividya,
M R Anala
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d8335.049420
Subject(s) - surprise , convolutional neural network , facial expression , computer science , artificial intelligence , expression (computer science) , pattern recognition (psychology) , feeling , face (sociological concept) , facial recognition system , speech recognition , machine learning , psychology , communication , social psychology , social science , sociology , programming language
Human feelings are mental conditions of sentiments that emerge immediately as opposed to cognitive exertion. Some of the basic feelings are happy, angry, neutral, sad and surprise. These internal feelings of a person are reflected on the face as Facial Expressions. This paper presents a novel methodology for Facial Expression Analysis which will aid to develop a facial expression recognition system. This system can be used in real time to classify five basic emotions. The recognition of facial expressions is important because of its applications in many domains such as artificial intelligence, security and robotics. Many different approaches can be used to overcome the problems of Facial Expression Recognition (FER) but the best suited technique for automated FER is Convolutional Neural Networks(CNN). Thus, a novel CNN architecture is proposed and a combination of multiple datasets such as FER2013, FER+, JAFFE and CK+ is used for training and testing. This helps to improve the accuracy and develop a robust real time system. The proposed methodology confers quite good results and the obtained accuracy may give encouragement and offer support to researchers to build better models for Automated Facial Expression Recognition systems.