###### Abstract  
Nowadays, _portrait drawing_ 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 _Portrait Drawing Learning Assistant System (PDLAS)_ for guiding novices by providing _auxiliary lines_ of facial features, generated by utilizing _OpenPose_ and _OpenCV_ libraries. For _PDLAS_, we have also presented the _exactness assessment method_ to evaluate drawing accuracy using the _Normalized Cross-Correlation (NCC)_ algorithm. It calculates the _similarity score_ between the drawing result and the initial portrait photo. Unfortunately, the current method does not assess the _hair drawing_, although it occupies a large part of a portrait and often determines its quality. In this paper, we present a _hair drawing evaluation algorithm_ for the _exactness assessment method_ to offer comprehensive feedback to users in _PDLAS_. 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 _NCC score_ improvement in _PDLAS_ by modifying the face parts with low similarity scores from the exactness assessment method.  
p 1 2020  
have designed and implemented the _exactness assessment method_ that quantifies the similarity of various facial parts, such as a mouth, a nose, and eyes, between a user's drawing result and its face photo using the _normalized cross-correlation (NCC)_ algorithm [6]. This method extracts a rectangle called a _bounding box_ that surrounds each face part, and calculates the _NCC_ value between the bounding boxes in the face photo and the drawing result as the _similarity score_ in the drawing result for evaluations. Unfortunately, the currently implemented method does not assess the _hair drawing_, although it occupies a large part of a portrait and often determines its quality. In this paper, we present a _hair drawing evaluation algorithm_ for the _exactness assessment method_ to offer comprehensive feedback to users in _PDLAS_. The procedure for extracting the bounding box is newly defined. In addition, to emphasize hair lines, the _texture_ of the bounding box is extracted by computing the eigenvalues and eigenvectors of the hair image for the _NCC_ algorithm application. For evaluations, we applied the proposed _hair drawing evaluation algorithm_ to the drawing results with _PDLAS_ of seven students at Okayama University, Japan, and confirmed its validity. In particular, by comparing the _NCC_ values before and after considering the _texture_, we verified the effectiveness of the evaluation improvement. As an overall evaluation of _PDLAS_, we asked three students to repeat modifying the face parts by _PDLAS_ that give low similarity scores by the exactness assessment method. The results observed _NCC_ show improvements among them. This paper is structured as follows:  
Section 2 introduces related works in the literature. Section 3 reviews _Portrait Drawing Learning Assistant System (PDLAS)_. Section 4 proposes the _hair drawing evaluation algorithm_. Section 5 evaluates the proposal through applications to portrait drawing results by students. Section 6 discusses the overall evaluation of _PDLAS_ with the _drawing exactness evaluation method_. Finally, Section 8 concludes this paper and future works.