<![CDATA[Facial Emotion Recognition (FER) is an important field within computer vision and human–computer interaction that focuses on the automatic recognition of human emotional expressions through facial images. This study presents a comparative analysis of two Convolutional Neural Network (CNN) architectures, namely Mini-Xception and EfficientNetB4, for real-time facial emotion classification using the RAF-DB (Real-world Affective Faces Database) dataset. Mini-Xception was employed as a lightweight model with lower computational requirements, whereas EfficientNetB4 utilized a transfer learning approach to achieve superior classification performance. The RAF-DB dataset consists of seven primary emotion categories: angry, disgust, fear, happy, neutral, sad, and surprise. The preprocessing stage included facial image resizing, grayscale conversion for Mini-Xception, RGB normalization for EfficientNetB4, and the application of data augmentation techniques to improve model generalization capability. Experimental results demonstrated that Mini-Xception achieved a validation accuracy of 52.12%, while EfficientNetB4 attained a validation accuracy of 86.02%. In real-time implementation using a webcam and OpenCV, Mini-Xception exhibited advantages in inference speed, whereas EfficientNetB4 produced more stable and accurate emotion predictions. The findings indicate a trade-off between computational efficiency and classification performance. Therefore, EfficientNetB4 is more suitable for systems requiring high classification accuracy, while Mini-Xception is more appropriate for real-time applications operating under limited computational resources.]]>
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