Facial Expression Detection of Autism Children Using ResNet-50 in Convolutional Neural Network Algorithm - Ekawati Prihatini; Selamat Muslimin; Muhammad Rizki Darmawan (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2025)
Facial expression detection in children with autism presents unique challenges due to limitations in verbal communication and social responses. This study develops a Convolutional Neural Network (CNN) model using the ResNet-50 architecture to improve the recognition accuracy of five expression categories: angry, fear, sad, neutral, and happy. A dataset of 3,030 images was divided into training and testing sets (60:40), with data augmentation and hyperparameter tuning applied using the Adam optimizer. The model achieved 89% validation accuracy and 84.49% testing accuracy, along with 86.78% precision and 80.69% recall. Evaluation on 25 test images showed an 84% success rate. These results indicate that ResNet‑50 effectively extracts facial features and classifies expressions with high accuracy, demonstrating potential as a communication aid in autism therapy. Future improvements include adding more diverse training data and optimizing model parameters.