Experimentation on Hand Drawn Sketches by Children to Classify Draw-a-Person Test Images in Psychology

Classification of hand drawn sketches with respect to content quality is extremely challenging task, comparing to usual image classification methods. In brief, we need to train computational device to able to classify the images of the same object into different classes with respect their content quality. In this paper we tested several methods of image classification, using machine learning and computer vision algorithms, to classify Draw-a-Person test images sketched by primary school students in Nigeria, aged 4 to 11 years. We collected 1000 original sketches and manually classified them (using guidelines from existing literature) according to the ages (8 classes) before testing this dataset on a computational device. The highest accuracy achieved in this experiment was 62%. We achieved this result with novel method, where we used Bag of Visual Words and K-means algorithm to count key-points on each sketch. We strongly believe that this challenging task needs further research to improve classification accuracy, we, therefore, release the complete dataset of sketches to the community.