A concise and visual computational interpretation of emotional expression in hand-drawn lines

In the realms of visual arts and psychology, it's acknowledged that lines and their properties significantly impact human emotional responses. Historically, various characteristics of lines have been proposed for emotional expression research, yet, the pivotal technique in computer vision, Scale-Invariant Feature Transform (SIFT), has frequently been disregarded. computing SIFT key points and their orientations on lines unveils the morphological traits of lines and deciphers their connection with specific emotional states. This method is not only concise in computation but also offers intuitive visualizations and explanations. Our investigation delves into the crucial role of SIFT features in conveying emotions from a computational vision standpoint. By scrutinizing SIFT features extracted from hand-drawn line images, we examined the trends in the quantity and orientation of feature points, and vividly illustrated how these points influence line morphology. Logistic regression analysis has further exposed the linkage between feature points and emotional dimensions such as pleasure and arousal. The results underscore the significance of SIFT features in emotional expression, facilitating the advancement of affective computing applications, as well as emotion recognition and analysis grounded in line features.