Age Estimation Based on Local Radon Features of Facial Images

  • Asuman Günay
  • Vasif V. Nabiyev
Conference paper


This paper proposes a new age estimation method relying on regional Radon features of facial images and regression. Radon transform converts a pixel represented image an equivalent, lower dimensional and more geometrically informative Radon pixel image and it brings a large advantage achieving global geometric affine invariance. Proposed method consists of four modules: preprocessing, feature extraction with Radon transform, dimensionality reduction with PCA and age estimation with multiple linear regression. We conduct our experiments on FG-NET, MORPH and FERET databases and the results have shown that proposed method has better results than many conventional methods on all databases.


Age estimation Radon transform PCA Regression 


  1. 1.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  2. 2.
    Al-Shaykh, O., Doherty, J.: Invariant image analysis based on radon transform and svd. IEEE Trans. Circuit. Syst. II Analog Digit. Sig. Process. 43(2), 123–133 (1996)Google Scholar
  3. 3.
    Beylkin, G.: Discrete Radon transform. IEEE Trans. Acoust. Speech Sig. Process. ASSP 35(2), 162–171 (1987)Google Scholar
  4. 4.
    Cai, D., He, X., Han, J., Zhang, H.-J.: Orthogonal laplacianfaces for face recognition. IEEE Trans. Image Process. 15(11), 3608–3614 (2006)CrossRefGoogle Scholar
  5. 5.
    Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. PAMI 23(6), 681–685 (2001)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Fu, Y., Xu, Y., Huang, T.S.: Estimating human age by manifold analysis of face pictures and regression on aging features. In: Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1383–1386 (2007)Google Scholar
  8. 8.
    Fu, Y., Huang, T.S.: Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multimedia 10(4), 578–584 (2008)CrossRefGoogle Scholar
  9. 9.
    Fukai, H., Takimoto, H., Mitsukura, Y., Fukumi, M.: Apparent age estimation system based on age perception. In: Proceedings of SICE 2007 Annual Conference Takanatsu, pp. 2808–2812 (2007)Google Scholar
  10. 10.
    Gao, F., Ai, H.: Face age classification on consumer images with gabor feature and fuzzy LDA method. In: Proceedings of 3rd International Conference on Advances in Biometrics LNCS’5558, pp. 132–141. Alghero, Italy (2009)Google Scholar
  11. 11.
    Geng, X., Zhou, Z.H., Zhang, Y., Li, G., Dai, H.: Learning from facial aging patterns for automatic age estimation. In: Proceedings of ACM Conference on Multimedia, pp. 307–316 (2006)Google Scholar
  12. 12.
    Geng, X., Zhou, Z.H., Miles, K.S.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2234–2240 (2007)CrossRefGoogle Scholar
  13. 13.
    Guo, G., Fu, Y., Huang, T.S., Dyer, C.R.: Locally adjusted robust regression for human age estimation. In: IEEE Workshop on Applications of Computer Vision, WACV’08, pp. 1–6. Copper Mountain (2008)Google Scholar
  14. 14.
    Ju, C.H., Wang, Y.H.: Automatic age estimation based on local feature of face image and regression. In: International Conference on Machine Learning and Cybernetics, pp. 885–888. Hebei University, Baoding (2009)Google Scholar
  15. 15.
    Kwon, Y.H., Lobo, N.V.: Age classification from facial images. Comput. Vis. Image Underst. 74(1), 1–21 (1999)Google Scholar
  16. 16.
    Lanitis, A., Taylor, C., Cootes, T.: Toward automatic simulation of aging effects on face images. IEEE Trans. PAMI 24(4), 442–455 (2002)Google Scholar
  17. 17.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)CrossRefGoogle Scholar
  18. 18.
    Ricanek, K. Jr., Tesafaye, T.: MORPH: a longitudinal image database of normal adult age-progression. In: IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK, pp. 341–345 (2006)Google Scholar
  19. 19.
    Yang, Z., Ai, H.: Demographic classification with local binary patterns. In: Proceedings of International Conference on Advances in Biometrics, LNCS’4642, Seoul, Korea, pp. 464–473 (2007)Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Computer EngineeringKaradeniz Technical UniversityTrabzonTurkey

Personalised recommendations