Abstract
Automatic Facial Expression Recognition (FER) is an active research area in artificial intelligence and image processing. One of the main challenges in FER is the automatic feature extraction. In this paper, the proposed 10 –layer Convolutional Neural Network (CNN) architecture can automatically detect important features without human supervision. This research uses Extended Cohn-Kanade (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset for facial expression recognition. The proposed architecture achieved an accuracy of 99.3% and 78.1% in classifying facial expressions in CK+ and JAFFE datasets.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
