Intelligent recognition of portrait sketch components for child autism assessment

For autistic children with slow language function, it is a classic and easy way to understand the development of their cognitive ability through simple painting experiments. Due to the lack of professional evaluators for painting assessment of autistic children, this article research and implement an intelligent assessment system for child autism through recognition of portrait sketch components. A portrait sketch database is constructed with the sample size of 30,400 after data expansion. The data consists of two formats: stroke vector sequence and 2D image. Then we propose a joint model coupled with LSTM and CNN features to automatically segment the portrait sketch components. It can perform better segmentation for exaggerated proportion and incomplete components samples. Finally, according to the evaluation criteria of the painting, we design an assessment model for child autism. The experiments are conducted in cooperation with relevant rehabilitation institutions to verify the effectiveness of the system. The analysis results show that our painting assessment system has a good ability to identify autistic tendencies. It can accurately evaluate children's autistic tendencies through “draw-a-man” experiments.