<![CDATA[Facial expression prediction has become a vital area in computer vision, with applications spanning healthcare, security, and human-computer interaction. This study proposes a robust system for binary facial expression prediction using a combination of classical computer vision techniques and deep learning. The system employs the Haar Cascade algorithm for face detection and ResNet50, a 50-layer deep residual network, for feature extraction. Support Vector Machines (SVM) with a radial basis function kernel are used for classification. Using the 4,000 tagged images from the GENKI dataset, preprocessing and data augmentation improved the model's capacity for generalization. Experimental results demonstrate the system’s effectiveness, achieving a test accuracy of 94.65%. The robust integration of classical and modern techniques ensures computational efficiency while maintaining high performance. For real-world applications, this method provides a scalable solution that tackles issues including lighting fluctuation, position, and expression variation.]]>
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