<![CDATA[Facial expressions are an important component of nonverbal communication that enable humans to understand each other's emotional states intuitively. Although humans can easily recognize expressions such as smiles or frowns, replicating this ability in computational systems remains a complex challenge. Therefore, an automated system capable of accurately and efficiently identifying facial expressions is needed. This research aims to compare the accuracy of CNN and SVM methods in facial expression recognition using the JAFFE dataset, which is limited to one demographic (Japanese women) with 284 images (80% for training, 10% for validation, and 10% for testing). CNN extracts features through convolution and pooling processes, while SVM is used as a classification algorithm based on statistical learning. The recognition process is divided into three main stages: data preprocessing, feature extraction, and facial expression classification. The system recognizes seven emotional categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. Results show that CNN outperforms SVM with an accuracy of 86%, while SVM achieves 81%. The limitations of the dataset may affect generalizability, and further research can use larger, more diverse datasets]]>
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