Identification of Autism Spectrum Disorder through Facial Morphology

Abstract

Autism spectrum disorder ASD has remained a popular research area for the last two decades. Autism is a severe spectrum disorder that affects communication, intellectual and social skills. Each patient exhibits the same facial abnormalities, allowing experts to diagnose the condition with just an image. When it comes to ASD diagnosis from a facial features analysis perspective, key challenges include identifying physical facial features, analysing data to try to correlate these observations with ASD occurrence and classifying children’s images into autistic and non-autistic. The focus of our thesis lies in the detection of ASD in children by facial morphology without any medical tests and psychological therapy using only deep learning techniques. We used the Autism Children dataset, which includes autistic and nonautistic children facial images. We applied transfer learning approaches on various pre-trained convolutional neural networks to extract features from autistic and nonautistic children’s faces and classify them. With two distinct transfer learning based approaches, we were able to determine the best feature extraction and classification models for autistic and non-autistic children’s face images. MobileNet was employed as a feature extractor and these features were then passed to the SVM classifier for classification, which produced the best results and VGG-16 was fine-tuned with three new dense layers. Both strategies outperformed existing techniques in terms of accuracy. Our proposed method for the first screening of ASD patients saves both money and valuable time.