Modeling of Facial Wrinkles for Applications in Computer Vision



Analysis and modeling of aging human faces have been extensively studied in the past decade for applications in computer vision such as age estimation, age progression and face recognition across aging. Most of this research work is based on facial appearance and facial features such as face shape, geometry, location of landmarks and patch-based texture features. Despite the recent availability of higher resolution, high quality facial images, we do not find much work on the image analysis of local facial features such as wrinkles specifically. For the most part, modeling of facial skin texture, fine lines and wrinkles has been a focus in computer graphics research for photo-realistic rendering applications. In computer vision, very few aging related applications focus on such facial features. Where several survey papers can be found on facial aging analysis in computer vision, this chapter focuses specifically on the analysis of facial wrinkles in the context of several applications. Facial wrinkles can be categorized as subtle discontinuities or cracks in surrounding inhomogeneous skin texture and pose challenges to being detected/localized in images. First, we review commonly used image features to capture the intensity gradients caused by facial wrinkles and then present research in modeling and analysis of facial wrinkles as aging texture or curvilinear objects for different applications. The reviewed applications include localization or detection of wrinkles in facial images, incorporation of wrinkles for more realistic age progression, analysis for age estimation and inpainting/removal of wrinkles for facial retouching.


Facial Image Local Binary Pattern Active Appearance Model Marked Point Process Skin Texture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    N. Batool, R. Chellappa, A Markov point process model for wrinkles in human faces, in 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, Orlando, 30 September–3 October 2012, pp. 1809–1812. doi: 10.1109/ICIP.2012.6467233
  2. 2.
    N. Batool, R. Chellappa, Modeling and detection of wrinkles in aging human faces using marked point processes, in ECCV Workshops (2) (2012), pp. 178–188Google Scholar
  3. 3.
    N. Batool, R. Chellappa, Detection and inpainting of facial wrinkles using texture orientation fields and Markov random field modeling. IEEE Trans. Image Process. 23(9), 3773–3788 (2014). doi: 10.1109/TIP.2014.2332401 MathSciNetCrossRefGoogle Scholar
  4. 4.
    N. Batool, R. Chellappa, Fast detection of facial wrinkles based on gabor features using image morphology and geometric constraints. Pattern Recogn. 48(3), 642–658 (2015). doi: 10.1016/j.patcog.2014.08.003 CrossRefGoogle Scholar
  5. 5.
    N. Batool, S. Taheri, R. Chellappa, Assessment of facial wrinkles as a soft biometrics, in 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, Shanghai, 22–26 April 2013, pp. 1–7. doi: 10.1109/FG.2013.6553719 Google Scholar
  6. 6.
    L. Boissieux, G. Kiss, N. Thalmann, P. Kalra, Simulation of skin aging and wrinkles with cosmetics insight, in Computer Animation and Simulation 2000, Eurographics, ed. by N. Magnenat-Thalmann, D. Thalmann, B. Arnaldi (Springer, Vienna, 2000), pp. 15–27CrossRefGoogle Scholar
  7. 7.
    C. Chen, W. Yang, Y. Wang, K. Ricanek, K. Luu, Facial feature fusion and model selection for age estimation, in 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011) (2011), pp. 200–205Google Scholar
  8. 8.
    T.F. Cootes, G.J. Edwards, C.J. Taylor, Active appearance models, in Proceedings of the 5th European Conference on Computer Vision - ECCV’98, Freiburg, vol. II 2–6 June 1998, pp. 484–498Google Scholar
  9. 9.
    G.O. Cula, P.R. Bargo, N. Kollias, Assessing facial wrinkles: automatic detection and quantification (2009). doi: 10.1117/12.811608 Google Scholar
  10. 10.
    G.O. Cula, P.R. Bargo, A. Nkengne, N. Kollias, Assessing facial wrinkles: automatic detection and quantification. Skin Res. Technol. 19(1), e243–e251 (2013). doi: 10.1111/j.1600-0846.2012.00635.x CrossRefGoogle Scholar
  11. 11.
    O.G. Cula, K.J. Dana, F.P. Murphy, B.K. Rao, Skin texture modeling. Int. J. Comput. Vision 62(1–2), 97–119 (2005). doi: 10.1007/s11263-005-4637-2 CrossRefGoogle Scholar
  12. 12.
    W. Freeman, E. Adelson, The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991)CrossRefGoogle Scholar
  13. 13.
    Y. Fu, N. Zheng, M-face: an appearance-based photorealistic model for multiple facial attributes rendering. IEEE Trans. Circuits Syst. Video Technol. 16(7), 830–842 (2006)CrossRefGoogle Scholar
  14. 14.
    Y. Fu, G. Guo, T.S. Huang, Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010). doi: Google Scholar
  15. 15.
    U. Hess Jr., R.B. Adams, A. Simard, M.T. Stevenson, R.E. Kleck, Smiling and sad wrinkles: age-related changes in the face and the perception of emotions and intentions. J. Exp. Soc. Psychol. 48(6), 1377–1380 (2012)CrossRefGoogle Scholar
  16. 16.
    A. Jain, U. Park, Facial marks: Soft biometric for face recognition, in 2009 16th IEEE International Conference on Image Processing (ICIP) (2009), pp. 37–40Google Scholar
  17. 17.
    S. Jeong, Y. Tarabalka, J. Zerubia, Marked point process model for facial wrinkle detection, in 2014 IEEE International Conference on Image Processing (ICIP) (2014), pp. 1391–1394. doi: 10.1109/ICIP.2014.7025278
  18. 18.
    S. Jeong, Y. Tarabalka, J. Zerubia, Marked point process model for curvilinear structures extraction, in Energy Minimization Methods in Computer Vision and Pattern Recognition - Proceedings of the 10th International Conference, EMMCVPR 2015, Hong Kong, 13–16 January 2015, pp. 436–449Google Scholar
  19. 19.
    L.I. Jiang, T.J. Stephens, R. Goodman, SWIRL, a clinically validated, objective, and quantitative method for facial wrinkle assessment. Skin Res. Technol. 19, 492–498 (2013). doi: 10.1111/srt.12073 Google Scholar
  20. 20.
    B. Klare, A.A. Paulino, A.K. Jain, Analysis of facial features in identical twins, in Proceedings of the 2011 International Joint Conference on Biometrics, IJCB ’11 (2011), pp. 1–8Google Scholar
  21. 21.
    Y.H. Kwon, N. da Vitoria Lobo, Age classification from facial images, in 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings CVPR ’94 (1994), pp. 762–767Google Scholar
  22. 22.
    Y.H. Kwon, N. da Vitoria Lobo, Age classification from facial images. Comput. Vis. Image Underst. 74(1), 1–21 (1999). doi: 10.1006/cviu.1997.0549 CrossRefGoogle Scholar
  23. 23.
    Z. Liu, Y. Shan, Z. Zhang, Expressive expression mapping with ratio images, in Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’01 (ACM, New York, 2001), pp. 271–276. doi: 10.1145/383259.383289 CrossRefGoogle Scholar
  24. 24.
    K. Luu, T.D. Bui, C. Suen, K. Ricanek, Combined local and holistic facial features for age-determination, in 2010 11th International Conference on Control Automation Robotics Vision (ICARCV) (2010), pp. 900–904Google Scholar
  25. 25.
    N. Magnenat-Thalmann, P. Kalra, J. Luc Leveque, R. Bazin, D. Batisse, B. Querleux, A computational skin model: fold and wrinkle formation. IEEE Trans. Inf. Technol. Biomed. 6(4), 317–323 (2002). doi: 10.1109/TITB.2002.806097 CrossRefGoogle Scholar
  26. 26.
    S. Mukaida, H. Ando, Extraction and manipulation of wrinkles and spots for facial image synthesis, in Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
  27. 27.
    C.C. Ng, M. Yap, N. Costen, B. Li, Automatic wrinkle detection using hybrid hessian filter, in 2014 Proceedings of the Asian Conference on Computer Vision ACCV, Singapore (2014)Google Scholar
  28. 28.
    T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  29. 29.
    H.C. Okada, B. Alleyne, K. Varghai, K. Kinder, B. Guyuron, Facial changes caused by smoking: a comparison between smoking and nonsmoking identical twins. Plast Reconstr. Surg. 132(5), 1085–1092 (2013)CrossRefGoogle Scholar
  30. 30.
    U. Park, A. Jain, Face matching and retrieval using soft biometrics. Inf. Forensics Secur. 5(3), 406–415 (2010)CrossRefGoogle Scholar
  31. 31.
    E. Patterson, A. Sethuram, K. Ricanek, F. Bingham, Improvements in active appearance model based synthetic age progression for adult aging, in Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS’09 (2009), pp. 104–108Google Scholar
  32. 32.
    P. Phillips, P. Flynn, K. Bowyer, R. Bruegge, P. Grother, G. Quinn, M. Pruitt, Distinguishing identical twins by face recognition, in 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011) (2011), pp. 185–192Google Scholar
  33. 33.
    N. Ramanathan, R. Chellappa, Modeling shape and textural variations in aging faces, in 8th IEEE International Conference on Automatic Face Gesture Recognition, 2008 (FG ’08) (2008), pp. 1–8Google Scholar
  34. 34.
    N. Ramanathan, R. Chellappa, S. Biswas, Computational methods for modeling facial aging: A survey. J. Vis. Lang. Comput. 20(3), 131–144 (2009)CrossRefGoogle Scholar
  35. 35.
    C. Robert, M. Bonnet, S. Marques, M. Numa, O. Doucet, Low to moderate doses of infrared a irradiation impair extracellular matrix homeostasis of the skin and contribute to skin photodamage. Skin Pharmacol. Physiol. 28(4), 196–204 (2015)CrossRefGoogle Scholar
  36. 36.
    A. Sethuram, E. Patterson, K. Ricanek, A. Rawls, Improvements and performance evaluation concerning synthetic age progression and face recognition affected by adult aging, in Advances in Biometrics, ed. by M. Tistarelli, M. Nixon. Lecture Notes in Computer Science, vol. 5558 (Springer, Berlin/Heidelberg, 2009), pp. 62–71Google Scholar
  37. 37.
    J. Suo, F. Min, S. Zhu, S. Shan, X. Chen, A multi-resolution dynamic model for face aging simulation, in IEEE Conference on Computer Vision and Pattern Recognition, 2007 (CVPR ’07) (2007), pp. 1–8Google Scholar
  38. 38.
    J. Suo, X. Chen, S. Shan, W. Gao, Learning long term face aging patterns from partially dense aging databases, in 2009 IEEE 12th International Conference on Computer Vision (2009), pp. 622–629Google Scholar
  39. 39.
    J. Suo, S.C. Zhu, S. Shan, X. Chen, A compositional and dynamic model for face aging. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 385–401 (2010)CrossRefGoogle Scholar
  40. 40.
    J. Suo, X. Chen, S. Shan, W. Gao, Q. Dai, A concatenational graph evolution aging model. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2083–2096 (2012)CrossRefGoogle Scholar
  41. 41.
    X. Tan, Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)Google Scholar
  42. 42.
    Y.L. Tian, T. Kanade, J. Cohn, Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 97–115 (2001). doi: 10.1109/34.908962 CrossRefGoogle Scholar
  43. 43.
    Y. Wu, N. Magnenat-Thalmann, D. Thalmann, A plastic-visco-elastic model for wrinkles in facial animation and skin aging, in Proceedings of the Second Pacific Conference on Fundamentals of Computer Graphics, Pacific Graphics ’94 (World Scientific, River Edge, 1994), pp. 201–213Google Scholar
  44. 44.
    L. Yin, S. Royt, M. Yourst, A. Basu, Recognizing facial expressions using active textures with wrinkles, in Proceedings of the 2003 International Conference on Multimedia and Expo, 2003 (ICME ’03), vol. 1 (2003), pp. 177–180. doi: 10.1109/ICME.2003.1220883
  45. 45.
    Y. Zhang, Q. Ji, Facial expression understanding in image sequences using dynamic and active visual information fusion, in Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003, vol. 2 (2003), pp. 1297–1304. doi: 10.1109/ICCV.2003.1238640

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Center for Medical Image Science and Visualization (CMIV)Linköpings Universitet/USLinköpingSweden
  2. 2.Department of Electrical and Computer Engineering and the Center for Automation ResearchUMIACS, University of MarylandCollege ParkUSA

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