Abstract
As the interest in one's appearance has recently increased, the demand for diagnosing skin conditions has also increased. However, conventional specialized skin diagnostic devices are generally expensive, and people have to visit a skin-care shop to diagnose their skin condition. This is time consuming and troublesome. In this paper, we propose a skin-roughness estimation method that uses a mobile-phone camera in daily environments. In order to achieve accurate evaluation, the illumination variation is alleviated using texture components of the facial skin image. We also propose a new feature-extraction method based on the gray-level co-occurrence matrix, which effectively measures the skin roughness from the texture components. The performance of the proposed method is compared with the conventional commonly used features, and we verify the superiority of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 13-22 |
| Number of pages | 10 |
| Journal | Journal of Visual Communication and Image Representation |
| Volume | 46 |
| DOIs | |
| Publication status | Published - 2017 Jul 1 |
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Keywords
- Gray-level co-occurrence matrix (GLCM)
- Skin image
- Skin roughness
- Texture domain
ASJC Scopus subject areas
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
Cite this
Robust skin-roughness estimation based on co-occurrence matrix. / Bae, Ji Sang; Lee, Sang Ho; Choi, Kang Sun; Kim, Jong-Ok.
In: Journal of Visual Communication and Image Representation, Vol. 46, 01.07.2017, p. 13-22.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Robust skin-roughness estimation based on co-occurrence matrix
AU - Bae, Ji Sang
AU - Lee, Sang Ho
AU - Choi, Kang Sun
AU - Kim, Jong-Ok
PY - 2017/7/1
Y1 - 2017/7/1
N2 - As the interest in one's appearance has recently increased, the demand for diagnosing skin conditions has also increased. However, conventional specialized skin diagnostic devices are generally expensive, and people have to visit a skin-care shop to diagnose their skin condition. This is time consuming and troublesome. In this paper, we propose a skin-roughness estimation method that uses a mobile-phone camera in daily environments. In order to achieve accurate evaluation, the illumination variation is alleviated using texture components of the facial skin image. We also propose a new feature-extraction method based on the gray-level co-occurrence matrix, which effectively measures the skin roughness from the texture components. The performance of the proposed method is compared with the conventional commonly used features, and we verify the superiority of the proposed method.
AB - As the interest in one's appearance has recently increased, the demand for diagnosing skin conditions has also increased. However, conventional specialized skin diagnostic devices are generally expensive, and people have to visit a skin-care shop to diagnose their skin condition. This is time consuming and troublesome. In this paper, we propose a skin-roughness estimation method that uses a mobile-phone camera in daily environments. In order to achieve accurate evaluation, the illumination variation is alleviated using texture components of the facial skin image. We also propose a new feature-extraction method based on the gray-level co-occurrence matrix, which effectively measures the skin roughness from the texture components. The performance of the proposed method is compared with the conventional commonly used features, and we verify the superiority of the proposed method.
KW - Gray-level co-occurrence matrix (GLCM)
KW - Skin image
KW - Skin roughness
KW - Texture domain
UR - http://www.scopus.com/inward/record.url?scp=85015639100&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015639100&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2017.03.003
DO - 10.1016/j.jvcir.2017.03.003
M3 - Article
AN - SCOPUS:85015639100
VL - 46
SP - 13
EP - 22
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
SN - 1047-3203
ER -