Yue Zhang, Zitong Kong, Nobuo Funabiki, Pradini Puspitaningayu
Nowadays, portrait drawing has gained popularity from various advantages in human sentiment cultivations and digital technology progresses including a tablet or a PC. However, it is still difficult for novices to get started from scratch. Previously, we have developed the Portrait Drawing Learning Assistant System (PDLAS) to assist portrait drawing by providing auxiliary lines as a reference. They are generated by running OpenPose and OpenCV to extract facial features. However, a method of evaluating the drawing accuracy is not implemented in PDLAS. In this paper, we present a drawing accuracy evaluation method of calculating the Normalized Cross-Correlation (NCC) for each face part to quantify the similarity between a user draw and its original face image. By localizing the range for each part, the result is improved. In the evaluation section, we analyze the scores assigned to different facial features by the NCC and identify the issues revealed by the scores for each user. Furthermore, we provide suggestions for improvement based on the weaknesses found in the users. Thus, we can provide specific feedback to the users. © 2024 IEEE.
Graduate School of Environmental, Life, Natural Sciences and Technology, Okayama University, Okayama, Japan; State University of Surabaya, Department of Electrical Engineering, Surabaya, Indonesia
