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September 5, 2025Open Access

Face Expression Recognition Classification Using Deep Learning

Key Points

The proposed model achieves high accuracy in classifying facial expressions, with significant implications for various applications.
Using a convolutional neural network, the system effectively analyzes face images to detect emotional states.
Performance indicators like accuracy and error rate were employed to assess the model against typical facial expression databases.
The findings support the relevance of deep learning in advancing human-computer interactions and security monitoring.

Abstract

This study seeks to design an intelligent system for face expression classification through the application of deep learning methods. Face images are analyzed to extract salient features and pattern them into facial expressions like happiness, sadness, anger, surprise, disgust and fear with a neutral face expression. A Convolutional Neural Network (CNN) framework was used because it is very effective in the analysis of images and in recognizing patterns. The model is trained from typical facial expression databases and tested with performance indicators like accuracy and error rate. The outcomes of the study reveal that the proposed model is highly accurate in the classification of feelings and it is therefore useful in the realistic applications of human-computer reciprocations, security and monitoring systems and in the care of mentally ill individuals.

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