Year: 2024
Type: article
Abstract: Emotional facial expression detection is a critical component with applications ranging from human-computer interaction to psychological research. This study presents an approach to emotion detection using the state-of-the-art YOLOv8 framework, a Convolutional Neural Network (CNN) designed for object detection tasks. This study utilizes a dataset comprising 2,353 images categorized into seven distinct emotional expressions: anger, contempt, disgust, fear, happiness, sadness, and surprise. The findings suggest that the YOLOv8 framework is a promising tool for emotional facial expression detection, with a potential for further enhancement through dataset augmentation. This research demonstrates the feasibility and effectiveness of using advanced CNN architectures for emotion recognition tasks.
DOI: 10.48084/etasr.8433
Authors: Aadil Alshammari, Muteb E. Alshammari
Institution: Northern Border University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $431
Funder: Northern Border University
Award: G5791967564
OpenAlex ID: W4403312554