Year: 2025
Type: article
Abstract: Researchers studying facial expression detection using deep neural networks have published findings in recent years that demonstrate how these methods get around the drawbacks of traditional machine learning techniques. In this paper, VGG-19 is used to monitor a driver's emotions and to increase its accuracy, which is trained using the similarity of the sample data. Data preparation comes initially, followed by the extraction of geometric characteristics and the detection of facial landmarks in input photos. The performance of the suggested VGG-19 classifier was compared to state-of-the-art techniques after these feature vectors were incorporated into it for facial expression classification. The findings demonstrate that our suggested approach performs comparably to deep learning techniques, which obtained 97.83% accuracy, 96.97% F1-score, 96.5% recall, and 97.73% precision.
Author: Sharon Sofia S S
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409894030