<![CDATA[This research focuses on the development of a facial expression recognition system based on image processing that is capable of identifying emotions with high accuracy. Facial recognition is a widely used technology for authentication and security, but it has potential applications in understanding emotional expressions. By utilizing Convolutional Neural Networks (CNN), the system is designed to detect and classify expressions such as happiness, sadness, anger, and neutrality in real time. The research stages include data collection of faces with various expressions, preprocessing, and training of the CNN model. Performance evaluation demonstrates that CNN outperforms traditional methods such as Viola-Jones and Support Vector Machine (SVM) under various lighting and angle conditions, achieving an average accuracy of 92%. These results prove the model's reliability in detecting emotional expressions with high precision. Further development is proposed to enhance performance, such as expanding the dataset variety and employing more advanced image processing techniques. Consequently, this system has the potential to make a positive impact on human-computer interaction.]]>
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