Automated Drawing Psychoanalysis via House-Tree-Person Test

Abstract

The increase of human psychological illness in today's fast paced and high stress world makes it essential to detect the warning signals of psychological problems. As the most representative drawing psychoanalysis method, House-Tree-Person (HTP) test is widely used in psychological assessment with the benefit of simplicity, non-verbal, and repeatability. HTP test can reveal the individual subconscious of the psychological state through the picture content of house, tree, and person drawn by the patient. Currently, HTP test is conducted by the therapist in person, which makes it time consuming and the results are mostly affected by the therapist's experience. Therefore, it is helpful and necessary to build an automated method to improve the objectivity, reliability, and efficiency of HTP test. In this paper, we propose an automated psychometric drawing screening method that forms the relationship between the psychological state and drawing feature. Specifically, we extract the key features including size, position, and shadow of the drawing, and then combine these features to construct a psychological state classifier. The proposed method can effectively screen out negative drawings for further diagnosis and treatment. Experiments are carried out on a builded dataset with the drawings from a psychological testing center of college. Experimental results demonstrate the effect and superiority of the proposed method.