Artificial Intelligence Contribution to Art-Therapy using Drawings of the House-Person-Tree Test

This paper applies computer vision and artificial intelligence algorithms to the HTP (House-Tree-Person) test, a projective test intended to measure different aspects of personality using drawings. The drawn pictures are assumed to represent the subject’s attitudes and feelings regarding themselves, other and their family. The House-Tree-Person evaluation uses "Qualitative Scoring," which is a subjective analysis influenced by the therapists that can be used to infer aggressive, depressive or anxious characteristics in the drawings. This paper is part of a larger project that aims to use artificial intelligence and image-processing techniques to support this process, hence reducing the bias factor from the equation. With the collaboration of the Department of Psychology at Istanbul Bilgi University, the project investigates on possible approaches to extract discriminative features out of HTP sketch images and it searches for a meaningful combination, which will support therapists in their diagnostic assessments. After data pre-processing, image classification of clinical HTP data was conducted using the ResNet152 model and achieving a test accuracy of 66%. Furthermore, the experiment of the detection of the "pen pressure" feature was performed using Skeletonization and morphological image processing; however, due to a lack of ground truth, the performance of the proposed algorithm is not determined yet.