Abstract :

Facial Expression Recognition Plays A Vital Role In Human-computer Interaction, Enabling Machines To Understand And Respond To Human Emotions Effectively. With The Advancement Of Artificial Intelligence And Computer Vision, Automated Facial Expression Recognition Systems Have Gained Significant Importance In Applications Such As Healthcare, Security, Education, And Entertainment. This Project Proposes The Design Of A Facial Expression Recognition System Using Deep Learning Techniques To Accurately Identify Human Emotions From Facial Images. The Proposed System Utilizes Image Datasets Containing Facial Expressions Such As Happiness, Sadness, Anger, Surprise, Fear, And Neutrality. Image Preprocessing Techniques Such As Resizing, Normalization, And Face Detection Are Applied To Enhance Data Quality. A Convolutional Neural Network (CNN) Model Is Employed To Extract Features And Classify Facial Expressions Due To Its Effectiveness In Image-based Tasks. The Model Is Trained And Tested Using An 80:20 Dataset Split To Ensure Reliable Performance Evaluation. The System’s Performance Is Evaluated Using Metrics Such As Accuracy, Precision, Recall, And F1-score. Experimental Results Demonstrate That The Deep Learning Model Achieves High Accuracy In Recognizing Facial Expressions, Making It Suitable For Realtime Applications. This Project Provides An Efficient And Scalable Solution For Emotion Recognition, Contributing To Advancements In Intelligent Systems And Improving Humancomputer Interaction.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1941-1947

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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