Abstract

Facial Expression Recognition is known for its efficiency and its stimulating job in this automated world. Facial Expressions are the easiest way for human being to express their feelings. Facial expression plays a major role in communicating non-verbally. This paper summarizes the Facial Expression Recognition (FER) techniques based on deep learning. FER technique’s performance is compared based on the amount of expressions recognized and the difficulty of algorithms in CNN. FER 2013 database is been used here. Recently, the CNN (Convolutional Neural Networks) has gained the reputation within the field of deep learning owing to their effective design and also the ability to produce smart results without manual feature extraction from the raw information. This paper investigates the effectiveness of CNN with Radial Basis Function for expression recognition. The experimental results shows that the proposed method provide relatively better accuracy for FER 2013 dataset.

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 call

Disclaimer: 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.