Analyzing Emotion Recognition Model Performance for Individuals with Down Syndrome

Emotion recognition plays an important role in human communication and emotional understanding. Recent advancements in deep learning and computer vision have enabled automated facial emotion recognition systems; however, most existing models are trained on datasets containing neurotypical individuals. This limits their effectiveness when applied to individuals with Down Syndrome due to differences in facial structure, muscle tone, and emotional expression patterns. This paper focuses on evaluating the performance of an existing deep learning–based facial emotion recognition system when applied to individuals with Down Syndrome. The system is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. The results highlight the need for population-specific validation and emphasize the importance of inclusive and reliable emotion recognition systems for assistive and healthcare applications.