A Study on Facial Wrinkle Detection using Active Appearance Models

AAM을 이용한 얼굴 주름 검출에 관한 연구

  • 이상범 (단국대학교 컴퓨터 과학과) ;
  • 김태묵 (단국대학교 컴퓨터 과학과)
  • Received : 2014.04.05
  • Accepted : 2014.07.20
  • Published : 2014.07.28


In this paper, a weighted value wrinkle detection method is suggested based on the analysis on the entire facial features such as face contour, face size, eyes and ears. Firstly, the main facial elements are detected with AAM method entirely from the input screen images. Such elements are mainly composed of shape-based and appearance methods. These are used for learning the facial model and for matching the face from new screen images based on the learned models. Secondly, the face and background are separated in the screen image. Four points with the biggest possibilities for wrinkling are selected from the face and high wrinkle weighted values are assigned to them. Finally, the wrinkles are detected by applying Canny edge algorithm for the interested points of weighted value. The suggested algorithm adopts various screen images for experiment. The experiments display the excellent results of face and wrinkle detection in the most of the screen images.


  1. Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11-15 (January, 1972)
  2. Lindeberg, Tony "Edge detection and ridge detection with automatic scale selection", International Journal of Computer Vision, 30, 2, pp 117-154, 1998.
  3. D. Cho, S. Lee "Facial Feature Tracking Using Adaptive Particle Filter and Active Appearance Model", Journal of Korea Robotics Society (2013) 8(2):104-115.
  4. H. P. Park, K. B. Kim, E. Y. Cha "Facial Feature Extraction using Multiple Active Appearance Model", JKIECS, Vol. 8, No. 8, 1201-1206, 2013.
  5. J. Ko, B. Suvdaa. "Accurate Face Pose Estimation and Synthesis Using Linear Transform Among Face Models", Journal of Korea Multimedia Society Vol. 15, No. 4, April 2012(pp. 508-515.)
  6. M. Yang, D. Kriegman, and N. Ahuja, "Detecting faces in Image: A Survey," IEEE Trans. on PAMI, Vol. 24, No. 1, pp. 34-58, 2002.
  7. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active Shape Models -Their Training and Application," Computer Vision, Graphics and Image Understanding, vol. 61, pp. 38-59, 1995.
  8. T. F. Cootes, G. Edwards, and C. Taylor, "Active Appearance Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp.681-685, 2001.
  9. G.J. Edwards, C.J. Taylor, and T.F. Cootes, "Face Recognition Using the Active Appearance Model," Proceedings of Fifth European Conference Computer Vision, vol. 2, pp. 581-695, 1998.
  10. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
  11. Lapiere CM. The ageing dermos: the main cause for the appearance of old skin. Br J Dermato. 122:5-11. 1990
  12. Shin-Jyung Kang, Ae-Jung Kim. "The effect of enhancing eye-wrinkle applying traditional herb medicine cosmetics".KAIS Vol.12 Num 1, 2011.
  13. C. Rother, V. Kolmogorov, and A. Blake, GrabCut: Interactive foreground extraction using iterated graph cuts, ACM Trans. Graph., vol. 23, pp. 309-314, 2004.
  14. Fisher, Perkins, Walker & Wolfart (2003). "Spatial Filters - Laplacian of Gaussian". Retrieved 2010-09-13.
  15. "Image Transforms - Hough Transform". Retrieved 2009-08-17.