DOI QR코드

DOI QR Code

Detection of Abnormal Region of Skin using Gabor Filter and Density-based Spatial Clustering of Applications with Noise

가버 필터와 밀도 기반 공간 클러스터링을 이용한 피부의 이상 영역 검출

  • Jeon, Minseong (Dept. of Computer Science, Chungbuk National University) ;
  • Cheoi, Kyungjoo (Dept. of Computer Science, Chungbuk National University)
  • Received : 2018.01.12
  • Accepted : 2017.01.23
  • Published : 2018.02.28

Abstract

In this paper, we suggest a new system that detects abnormal region of skim. First, an illumination elimination algorithm which uses LAB color model is processed on input facial image to obtain robust facial image for illumination, and then gabor filter is processed to detect the reactivity of discontinuity. And last, the density-based spatial clustering of applications with noise(DBSCAN) algorithm is processed to classify areas of wrinkles, dots, and other skin diseases. This method allows the user to check the skin condition of the images taken in real life.

Keywords

References

  1. Investigation of Lutronics's Interest in Skin Beauty, http://stock.hankyung.com/news/app/newsview.php?aid=2014121252216, (accessed Dec., 12, 2014).
  2. M. Ester, H.P. Kriegel, J. Sander, and X. Xu, "A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Vol. 96, No. 34, pp. 226-231, 1996.
  3. Q. Wen, D. Ming, and J. Chen, "A Novel Fusion Approach for Segmenting Dermoscopy Image Based on Region Consistency," Proceeding of IEEE 2013 International Conference on Computational Problem-Solving, pp. 267-270, 2013.
  4. Y.W. Bae and B.J. Jo, "Skin Wrinkle Detection Using Dermatologic Magnifier Based on Variable Polarization and Optical Magnification," Proceeding of Conference of Korean Institute of Electrical Engineers, pp. 190-192, 2006.
  5. Y.H. Choi and I.J. Hwang, "A Scheme of Extracting Age-related Wrinkle Feature and Skin Age Based on Dermoscopic Images," Journal of Institute of Electrical and Electronic Engineers, Vol. 14, No. 4, pp. 332-338, 2010.
  6. H.S. Ku and H.G. Song, "A Study on the Eye-line Detection from Facial Image taken by Smart Phone," Journal of Korea Institute of Information and Communication Engineering, Vol. 15, No. 10, pp. 2231-2238, 2011. https://doi.org/10.6109/jkiice.2011.15.10.2231
  7. H.Y. Kim and S.R. Choi, "Face Detection Using The New Color Component be Fit to Skin Color," Proceeding of Conference of Korea Institute of Communication Sciences, pp. 347-347, 2014.
  8. K.M. Park, G.R. Yoon, and Y.B. Kim, "Skin Color Region Segmentation Using Classified 3D Skin," Journal of Korea Institute of Information and Communication Engineering, Vol. 14, No. 8, pp. 1809-1818, 2010. https://doi.org/10.6109/jkiice.2010.14.8.1809
  9. H.S. Jo, "Efficient Filter Design for Eye Detection in Color Images under Varying Lighting Conditions," Journal of Advanced Information Technology and Convergence, Vol. 6, No. 2, pp. 100-107, 2008.
  10. Y. Ryu, S.H. Lee, S.G. Kwon, and G.R. Kwon, "Skin Pigmentation Detection Using Projection Transformed Block Coefficient." Journal of Korea Multimedia Society Vol. 16, No. 9, pp. 1044-1056, 2013. https://doi.org/10.9717/kmms.2013.16.9.1044
  11. K.K. Lee, J.S. Yoo, J.G. Bae, J.S. Bae, and J.O. Kim, "Shooting Distance Adaptive Pore Extraction for Skin Condition Estimation," Journal of the Institute of Electronic Enginners of Korea, Vol. 52, No. 8, pp. 106-114, 2015.
  12. N. Batool and C. Rama, "Modeling and Detection of Wrinkles in Aging Human Faces Using Marked Point Processes," Proceedings of the 12th European Conference on Computer Vision, pp. 178-188, 2012.
  13. N. Batool and C. Rama, "Detection and Inpainting of Facial Wrinkles Using Texture Orientation Fields and Markov Random Field Modeling," IEEE Transactions on Image Processing, Vol. 23, No. 9, pp. 3773-3788, 2014. https://doi.org/10.1109/TIP.2014.2332401
  14. C.C. Ng, M.H. Yap, N. Costen, and B. Li, "Automatic Wrinkle Detection Using Hybrid Hessian Filter," Proceeding of 12th Asian Conference on Computer Vision, pp. 609-622, 2014.
  15. C.Y. Chang, S.C. Li, P.C. Chung, J.Y. Kuo, and Y.C. Tu, "Automatic Facial Skin Defect Detection System," Proceedings of the International Conference on Broadband, Wireless Computing, Communication and Applications, pp. 527-532, 2010.
  16. N. Alamdari, K. Tavakolian, M. Alhashim, and R. Razel-Rezai, "Detection and Classification of Acne Lesions in Acne Patients: A Mobile Application," Proceeding of 2016 IEEE International Conference on Electro Information Technology, pp. 0739-0743, 2016.
  17. J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, pp. 679-698, 1986. https://doi.org/10.1109/TPAMI.1986.4767851