Machine learning model for emotion detection and recognition using an enhanced Convolutional Neural Network

Sowmya B; Meeradevi; Sini Anna Alex; Anita Kanavalli; Supreeth S; Shruthi G; Rohith S

doi:10.62110/sciencein.jist.2024.v12.786

facial emotion detection using deep learning

Authors

Sowmya B J
Meeradevi Department of Artificial Intelligence and Machine Learning, M S Ramaiah Institute of Technology, 560054, India
Sini Anna Alex Department of CSE (AI & ML), M S Ramaiah Institute of Technology, 560054, India
Anita Kanavalli Department of Artificial Intelligence and Data Science, M S Ramaiah Institute of Technology, 560054, India
Supreeth S School of Computer Science and Engineering, REVA University, Yelahanka, Bengaluru, India
Shruthi G School of Computer Science and Engineering, REVA University, Yelahanka, Bengaluru, India
Rohith S Nagarjuna College of Engineering & Technology

DOI:

https://doi.org/10.62110/sciencein.jist.2024.v12.786

Keywords:

Emotion detection, Face recognition, Electric signal, Artificial intelligence system, Deep Learning

Abstract

Emotion expression recognition has been a challenging task in recent years due to large intraclass variation and persistent difficulty. Most studies fail on datasets with image variations and partial faces, but work best on controlled datasets. Recent work using deep learning models has improved emotion recognition by developing a mini-Xception based on Xception and Convolution Neural Network (CNN). This system can focus on important parts like the face, performing face recognition and emotion classification simultaneously. A visualization method is used to distinguish between different emotions based on classifier results. An experimental study on the FER-2013 dataset demonstrated that the mini-Xception algorithm successfully performed all tasks, including emotion recognition and classification, with an accuracy of about 95.60%.

URN:NBN:sciencein.jist.2024.v12.786