Recognition of Hand-drawn images of infants using Convolutional Neural Networks

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Mi-Hwa Song


The recognition rate of children's paintings is significantly lower than that of adults' paintings due to their unique characteristics. According to the research on children's art, children's paintings are self-centered and have many features different from those of adults, such as a lot of exaggerated expressions. In this paper, we introduce a method to increase the recognition rate of these children's pictures using deep learning. In order to improve the low recognition rate of children's pictures, a pre-processor that generalizes children's unique features was created to primarily purify the data, and classified into 250 using the Convolutional Neural Network, which is actively researched in the field of image recognition. High accuracy was obtained as a result of securing and executing sketch drawings of 80 adults for each object. Through this study, it is expected that not only improving the recognition ability for infant pictures, but also measuring learning ability and child development through children's drawings, and child psychotherapy through emotional recognition.

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