Automated Facial Wrinkles Annotator

  • Moi Hoon YapEmail author
  • Jhan Alarifi
  • Choon-Ching Ng
  • Nazre Batool
  • Kevin Walker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)


This paper presents an automated facial wrinkles annotator for coarse wrinkles, fine wrinkles and wrinkle depth map extraction. First we extended Hybrid Hessian Filter by introducing a multi-scale filter to isolate the coarse wrinkles from fine wrinkles. Then we generate a wrinkle probabilistic map. When evaluated on 20 high resolution full face images (10 from our in-house dataset and 10 from FERET dataset), we achieved good accuracy when the result of coarse wrinkles was validated with manual annotation. Furthermore, we visually illustrate the ability of the annotator in detecting fine wrinkles. This paper advances the field by automate the localisation of the fine wrinkles, which might not be possible to annotate manually. Our automated facial wrinkles annotator will be beneficial to large-scale data annotation and cosmetic applications.


Wrinkles annotator Hessian filter Wrinkles depth 



This work was supported by the Royal Society Industry Fellowship (IF160006). The authors would like to thanks Phillips et al. [11] for the FERET dataset.


  1. 1.
    Alarifi, J.S., Goyal, M., Davison, A.K., Dancey, D., Khan, R., Yap, M.H.: Facial skin classification using convolutional neural networks. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 479–485. Springer, Cham (2017). Scholar
  2. 2.
    Albert, A., Ricanek, K., Patterson, E.: A review of the literature on the aging adult skull and face: implications for forensic science research and applications. Forensic Sci. Int. 172(1), 1–9 (2007)CrossRefGoogle Scholar
  3. 3.
    Batool, N., Chellappa, R.: A Markov point process model for wrinkles in human faces. In: 2012 19th IEEE International Conference on Image Processing, pp. 1809–1812. IEEE (2012)Google Scholar
  4. 4.
    Batool, N., Chellappa, R.: Detection and inpainting of facial wrinkles using texture orientation fields and Markov random field modeling. IEEE Trans. Image Process. 23(9), 3773–3788 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cula, G.O., Bargo, P.R., Nkengne, A., Kollias, N.: Assessing facial wrinkles: automatic detection and quantification. Skin Res. Technol. 19(1), e243–e251 (2013)CrossRefGoogle Scholar
  6. 6.
    Batool, N., Chellappa, R.: Fast detection of facial wrinkles based on Gabor features using image morphology and geometric constraints. Pattern Recogn. 48(3), 642–658 (2015)CrossRefGoogle Scholar
  7. 7.
    Ng, C.C., Yap, M.H., Cheng, Y.T., Hsu, G.S.: Hybrid ageing patterns for face age estimation. Image Vis. Comput. 69, 92–102 (2018)., Scholar
  8. 8.
    Ng, C.-C., Yap, M.H., Costen, N., Li, B.: Automatic wrinkle detection using hybrid Hessian filter. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 609–622. Springer, Cham (2015). Scholar
  9. 9.
    Ng, C.C., Yap, M.H., Costen, N., Li, B.: Wrinkle detection using Hessian line tracking. IEEE Access 3, 1079–1088 (2015)CrossRefGoogle Scholar
  10. 10.
    Osman, O.F., Elbashir, R.M.I., Abbass, I.E., Kendrick, C., Goyal, M., Yap, M.H.: Automated assessment of facial wrinkling: a case study on the effect of smoking. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1081–1086, October 2017.
  11. 11.
    Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Manchester Metropolitan UniversityManchesterUK
  2. 2.Panasonic R&D Center SingaporeSingaporeSingapore
  3. 3.Scania CV ABSödertäljeSweden
  4. 4.Image Metrics Ltd.ManchesterUK

Personalised recommendations