Research on Skin Texture Classification by Gray Level Co-occurrence Matrix and the BP Neural Network
Qiaohua Liu, Tianhua Chen, Xiaoyi Wang, Jiping Xu, Li Wang, Yinmao Dong, Hong Meng
Available Online November 2015.
- https://doi.org/10.2991/tmcm-15.2015.8How to use a DOI?
- skin texture; gray level co-occurrence matrix; principal component analysis; BP neural network classifier
- It’s very common to use the skin texture of gray level co-occurrence matrix to calculate the four most representative eigenvalues of human facial skin image: energy, moment of inertia, correlation and entropy. To test whether the four eigenvalues can represent the skin texture information, the article designed a verification experiment: the article used comparison data included arithmetic average roughness(Ra), average roughness(Rz), and smooth depth data(Rt) measured from DERMATOP V3 of CK in Germany, and experimental data included the four eigenvalues, to do principal component analysis, respectively, for unrelated principal component as the input data of BP neural network classifier. The experimental results show that using the four eigenvalues, the classification accuracy is higher. The method using gray level co-occurrence matrix to extract facial skin texture eigenvalue can relatively reflect the degree of human facial texture state than texture information measured by DERMATOP V3, which provides a simple and effective method for the data acquisition of skin texture.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Qiaohua Liu AU - Tianhua Chen AU - Xiaoyi Wang AU - Jiping Xu AU - Li Wang AU - Yinmao Dong AU - Hong Meng PY - 2015/11 DA - 2015/11 TI - Research on Skin Texture Classification by Gray Level Co-occurrence Matrix and the BP Neural Network BT - 2015 International Conference on Test, Measurement and Computational Methods PB - Atlantis Press SP - 26 EP - 29 SN - 2352-538X UR - https://doi.org/10.2991/tmcm-15.2015.8 DO - https://doi.org/10.2991/tmcm-15.2015.8 ID - Liu2015/11 ER -