A Study on Autism Detection from Facial Images

Abstract

Abstract: Autism is a serious type of neurodevelopmental condition that disrupts cognitive functioning, language, and social behavior. Individuals with autism spectrum disorders have a broad range of intellectual functioning, from significant disability to outstanding abilities. The severity and long-term repercussions of ASD can be avoided with an early diagnosis. Medical professionals currently use a number of methods to predict autism, including brain scan analysis, autism diagnostic interviews, autism diagnostic observations, and physical facial trait analysis. These traditional ways of diagnosing autism are quite timeconsuming, expensive, and complicated. People with autism have a distinctive set of facial traits which distinguish them from normal ones. One of the most interesting areas of autism study is the application of facial traits as a physical indicator for autism diagnosis. The use of deep learning and machine learning has become increasingly widespread in recent years, particularly in the field of image classification. These algorithms are capable of identifying hidden autism patterns from vast amounts of facial data, making them useful as autism predictor. Hence, this study reviews the various autism prediction methods based on facial features using deep learning and machine learning techniques.