<![CDATA[Facial expressions represent one of the most significant forms of non-verbal communication, with psychologists identifying six universal expressions: happiness, sadness, surprise, anger, fear, and disgust. Recognizing these expressions presents considerable challenges due to the subtlety of facial movements and variations across individuals. This paper presents a deep learning-based system for facial expression recognition (FER) that employs convolutional neural networks (CNNs) to classify emotional states. We investigate both a novel CNN architecture developed from scratch and established transfer learning approaches, evaluating their performance on the FER-2013 dataset. Our experimental results demonstrate that the proposed custom CNN architecture achieves 72.93% accuracy when combined with comprehensive data augmentation techniques, outperforming several baseline models. The system shows particular strength in recognizing fundamental emotions while maintaining computational efficiency suitable for real-time applications.]]>
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