A Study on Improving Free-Hand Sketch Recognition of Infants Using Deep Learning
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Abstract
Due to its unique characteristics, infant paintings have a significantly lower recognition rate than adult images. According to the study of infant art, infant paintings have many features that are different from adult images, such as the appearance of many self-centered and exaggerated expressions. In this paper, we will introduce a method to improve the recognition rate of such children's drawings by utilizing deep learning. Create a pre-processor that generalizes the unique characteristics of the child to improve the low recognition rate of the infant figure, and primarily refine the data. High accuracy was obtained as a result of securing and executing 80 adult sketches for each of 250 classified items using CNN, which is often used for image recognition. Through this research, it is expected that it will be possible not only to improve the cognitive ability of infant figures, but also to measure learning ability and child development through infant drawings, and to utilize it in child psychotherapy through emotion recognition
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