A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System
Nowadays, <i>portrait drawing</i> has become increasingly popular as a means of developing artistic skills and nurturing emotional expression. However, it is challenging for novices to start learning it, as they usually lack a solid grasp of proportions and structural foundations of the...
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MDPI AG
2025-03-01
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| author | Yue Zhang Nobuo Funabiki Erita Cicilia Febrianti Amang Sudarsono Chenchien Hsu |
| author_facet | Yue Zhang Nobuo Funabiki Erita Cicilia Febrianti Amang Sudarsono Chenchien Hsu |
| author_sort | Yue Zhang |
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| description | Nowadays, <i>portrait drawing</i> has become increasingly popular as a means of developing artistic skills and nurturing emotional expression. However, it is challenging for novices to start learning it, as they usually lack a solid grasp of proportions and structural foundations of the five senses. To address this problem, we have studied <i>Portrait Drawing Learning Assistant System (PDLAS)</i> for guiding novices by providing <i>auxiliary lines</i> of facial features, generated by utilizing <i>OpenPose</i> and <i>OpenCV</i> libraries. For <i>PDLAS</i>, we have also presented the <i>exactness assessment method</i> to evaluate drawing accuracy using the <i>Normalized Cross-Correlation (NCC)</i> algorithm. It calculates the <i>similarity score</i> between the drawing result and the initial portrait photo. Unfortunately, the current method does not assess the <i>hair drawing</i>, although it occupies a large part of a portrait and often determines its quality. In this paper, we present a <i>hair drawing evaluation algorithm</i> for the <i>exactness assessment method</i> to offer comprehensive feedback to users in <i>PDLAS</i>. To emphasize hair lines, this algorithm extracts the texture of the hair region by computing the eigenvalues and eigenvectors of the hair image. For evaluations, we applied the proposal to drawing results by seven students from Okayama University, Japan and confirmed the validity. In addition, we observed the <i>NCC score</i> improvement in <i>PDLAS</i> by modifying the face parts with low similarity scores from the exactness assessment method. |
| format | Article |
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| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Algorithms |
| spelling | doaj-art-1af4700db73c433b942ba092ef29caf52025-08-20T03:40:43ZengMDPI AGAlgorithms1999-48932025-03-0118314310.3390/a18030143A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant SystemYue Zhang0Nobuo Funabiki1Erita Cicilia Febrianti2Amang Sudarsono3Chenchien Hsu4Department of Information and Communication Systems, Okayama University, Okayama 700-8530, JapanDepartment of Information and Communication Systems, Okayama University, Okayama 700-8530, JapanDepartment of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya 60111, IndonesiaDepartment of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya 60111, IndonesiaDepartment of Electrical Engineering, National Taiwan Normal University, Taipei 106308, TaiwanNowadays, <i>portrait drawing</i> has become increasingly popular as a means of developing artistic skills and nurturing emotional expression. However, it is challenging for novices to start learning it, as they usually lack a solid grasp of proportions and structural foundations of the five senses. To address this problem, we have studied <i>Portrait Drawing Learning Assistant System (PDLAS)</i> for guiding novices by providing <i>auxiliary lines</i> of facial features, generated by utilizing <i>OpenPose</i> and <i>OpenCV</i> libraries. For <i>PDLAS</i>, we have also presented the <i>exactness assessment method</i> to evaluate drawing accuracy using the <i>Normalized Cross-Correlation (NCC)</i> algorithm. It calculates the <i>similarity score</i> between the drawing result and the initial portrait photo. Unfortunately, the current method does not assess the <i>hair drawing</i>, although it occupies a large part of a portrait and often determines its quality. In this paper, we present a <i>hair drawing evaluation algorithm</i> for the <i>exactness assessment method</i> to offer comprehensive feedback to users in <i>PDLAS</i>. To emphasize hair lines, this algorithm extracts the texture of the hair region by computing the eigenvalues and eigenvectors of the hair image. For evaluations, we applied the proposal to drawing results by seven students from Okayama University, Japan and confirmed the validity. In addition, we observed the <i>NCC score</i> improvement in <i>PDLAS</i> by modifying the face parts with low similarity scores from the exactness assessment method.https://www.mdpi.com/1999-4893/18/3/143portrait drawingauxiliary linesOpenPoseOpenCV<i>normalized cross-correlation (NCC)</i>hair texture |
| spellingShingle | Yue Zhang Nobuo Funabiki Erita Cicilia Febrianti Amang Sudarsono Chenchien Hsu A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System Algorithms portrait drawing auxiliary lines OpenPose OpenCV <i>normalized cross-correlation (NCC)</i> hair texture |
| title | A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System |
| title_full | A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System |
| title_fullStr | A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System |
| title_full_unstemmed | A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System |
| title_short | A Hair Drawing Evaluation Algorithm for Exactness Assessment Method in Portrait Drawing Learning Assistant System |
| title_sort | hair drawing evaluation algorithm for exactness assessment method in portrait drawing learning assistant system |
| topic | portrait drawing auxiliary lines OpenPose OpenCV <i>normalized cross-correlation (NCC)</i> hair texture |
| url | https://www.mdpi.com/1999-4893/18/3/143 |
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