Understanding Facial Expression of Children with Autism Using Learning Theory

It is challenging for autistic individuals to gather socially and empathically, as we understand, so this subject matter differs from the identification of a common human facial expression. In our research, we have taken pictures of children and teenagers from school of special children based in Bangladesh, aged between 5 to 20 with Autism Spectrum Disorder (ASD) and identified their emotions from their images. Our objective is to find a technique that will allow their emotions to be accurately perceived and thus, make it easy for them including other individuals around them to interact socially without any barriers. In our paper, we implemented VGG16 and six Machine Learning Algorithms along with one Feature Extraction technique to detect Facial Expression of children with autism. The proposed system has showcased an accuracy of 75% for VGG16, 68% for Random Forest Classifier, 61% for Random Forest Classifier along with PCA, 61% for Support Vector Machine (SVM) and 67% for SVM with PCA, 56% for Logistic Regression and 53% for Logistic Regression along with PCA, 52% for Linear Discriminant Analysis (LDA) and 54% for LDA with PCA, 46% for Decision Tree and 50% for Decision Tree along with PCA and 46% for Gaussian Naïve Bayes and 38% for Gaussian Naïve Bayes along with PCA.