Emotion Recognition in Autistic Children Through Facial Expressions Using Advanced Deep Learning Architectures

Atypical and subtle facial expression patterns in individuals with autism spectrum disorder (ASD) pose a significant challenge for automated emotion recognition. This study evaluates and compares the performance of convolutional neural networks (CNNs) and transformer-based deep learning models for facial emotion recognition in this population. Using a labeled dataset of emotional facial images, we assessed eight models across four emotion categories: natural, anger, fear, and joy. Our results demonstrate that transformer models consistently outperformed CNNs in both overall and emotion-specific metrics. Notably, the Swin Transformer achieved the highest performance, with an accuracy of 0.8000 and an F1-score of 0.7889, significantly surpassing all CNN counterparts. While CNNs failed to detect the fear class, transformer models showed a measurable capability in identifying complex emotions such as anger and fear, suggesting an enhanced ability to capture subtle facial cues. Analysis of the confusion matrix further confirmed the transformers’ superior classification balance and generalization. Despite these promising results, the study has limitations, including class imbalance and its reliance solely on facial imagery. Future work should explore multimodal emotion recognition, model interpretability, and personalization for real-world applications. Research also demonstrates the potential of transformer architectures in advancing inclusive, emotion-aware AI systems tailored for autistic individuals.