| Abstrakt: |
Children with autism spectrum disorders (ASD) have problems understanding and emotionally communicating via facial expressions, until with their parents. The likelihood of children being classified with autism spectrum condition has risen considerably in past few years. Children with ASD have difficulty with verbal abilities making it Extremely Difficult to guess what they need emotionally and fast. There are a variety of ways for recognizing autism symptoms in children. Health providers usually employ facial expression-based ways to identify autistic children's feelings patterns. Sign language takes the form of facial expression, which reflects a human's internal emotions and thoughts. As far as facial expressions are concerned, facial expression recognition (FER) has lately gotten a lot of attention. As the quickest means of exchanging information of any type, which can then be analyzed using the most up to date deep learning techniques. Facial expression identification is a method that assists for discover children's feelings. Children with ASD, on the other hand, have difficulty recognizing facial emotions. Globally there are seven types of facial emotions: angry, disgusted, pleased, sad, frightened, astonished, and indifferent. observed most researchers utilized CNNs and other deep learning approaches to help identify facial expressions and emotions in photos or videos of autistic children. This paper provides a survey of the research on various approaches for newly developed technologies that aim to recognize human emotions through facial expressions. [ABSTRACT FROM AUTHOR] |
