Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6551–6573 | Cite as

Age estimation with dynamic age range

Article

Abstract

Age estimation has been widely used and became more and more important, for its usefulness in various applications. However, accurately predict the age for an unlabeled image is difficult, because there are many factors that have impact on the appearance of a person. Some people look younger than his/her true age, while the others look much older. Therefore, predict an age group or a specific age for a facial image is not good enough. In this paper, we propose a new method to estimate the age of facial image into a dynamic range or a discrete age set rather than a single age or age group. Furthermore, we introduce a new measurement, i.e. Confidence Interval/Confidence Level to evaluate the performance of proposed method. Our experimental results show that the proposed method is promising.

Keywords

Age estimation Density peak Local binary pattern(LBP) Confidence interval/confidence level 

References

  1. 1.
    Choi SE, Lee YJ (2011) et al Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn 44:1262–1281CrossRefMATHGoogle Scholar
  2. 2.
    Chen YW, Lai DH, Qi H et al A new method to estimate ages of facial image for large database. Multimed Tools Appl 2015:1–19. doi:10.1007/s11042-015-2485-9
  3. 3.
    Chen Y, Jixiang D (2014) A new method for classifying chinese text based on semantic topics and density peaks. Int J Appl Math Mach Lear 1(1):35–54Google Scholar
  4. 4.
    Chen Y-L, Hsu C-T (2013) Subspace learning for facial age estimation via pairwise age ranking. IEEE Trans Inf Forensic Secur 8(12):2164–2176CrossRefGoogle Scholar
  5. 5.
    Fukai H, Takimoto H, Mitsukura Y, Fukumi M (2007) An apparent age estimation system using the evolutionary algorithm. In: International Conference on Control, Automation and Systems(ICCAS 2007), pp 2146–2149Google Scholar
  6. 6.
    Gunay A (2008) Automatic age classification with LBP Computer and Information Sciences, 2008. ISCIS’08. In: IEEE 23rd International Symposium on, pp 1–4Google Scholar
  7. 7.
    Gao F, Ai HZ (2009) Face age classification on consumer images with gabor feature and fuzzy LDA method. Lecture Notes In Computer Science. In: Proceedings of the Third International Conference on Advances in Biometrics(ICB 2009), pp 132–141Google Scholar
  8. 8.
    Guo GD, Fu Y, Dyer CR, Huang TS (2008) Image-based humanage estimation by manifold learning and locally adjusted robust regression. IEEE Trans Image Process 17(7):1178–1188MathSciNetCrossRefGoogle Scholar
  9. 9.
    Geng X, Zhou Z-H, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell (TPAMI 2007) 29(12):2234–2240CrossRefGoogle Scholar
  10. 10.
    Geng X, Yin C, Zhou Z-H (2013) Facial Age Estimation by Learning from Label Distributions. IEEE Trans Pattern Anal Mach Intell (IEEE TPAMI 2013) 35(10):2401–2412CrossRefGoogle Scholar
  11. 11.
    Jiwen L, Tan Y-P (2013) Ordinary preserving manifold analysis for human age and head pose estimation. IEEE Trans Human-Mach Syst 43(2):249–258CrossRefGoogle Scholar
  12. 12.
    Kwon YH, Lobo NB (1999) Age classification from facial images. Comput Vis Pattern Recogn 74(1):1–21Google Scholar
  13. 13.
    Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24(4):442–455CrossRefGoogle Scholar
  14. 14.
    Lian HC, Lu BL (2005) Age estimation using a min-max modular support vector machine. In: 12th International Conference on Neural Information Processing(ICONIP 2005), pp 83–88Google Scholar
  15. 15.
    Liu K-H, Yan S, Jay Kuo C-C (2014) Age group classification via structured fusion of uncertainty-driven shape features and selected surface features. In: IEEE Winter Application and Computer Vision Conference(WACV 2014), pp 445–452Google Scholar
  16. 16.
    Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern Part B: Cybern 34(1):621–628CrossRefGoogle Scholar
  17. 17.
    Luu K, Ricanek Jr K, Bui TD, Suen CY (2009) Agee stimation using active appearance models and support vector machine regression. In: Proceedingsof the IEEE Third International Conference on Biometrics: Theory, Applications, and Systems(BTAS 2009), pp 1–5Google Scholar
  18. 18.
    Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344:1492–1496CrossRefGoogle Scholar
  19. 19.
    Ricanek K, Tesafaye T MORPH: A longitudinal image database of normal adult age-progression. In: IEEE Internationl Conference Automatic Face Gesture Recognition(FG 2006)Google Scholar
  20. 20.
    Suo J, Wu T, Zhu S, Shan S, Chen X, Gao W (2008) Design sparse features for age estimation using hierarchical face model. In: Proceedings of the Eighth IEEE International Conference on Automatic Face & Gesture Recognition(FG 2008), pp 17–19Google Scholar
  21. 21.
    Wang X, Ly V, Lu G, Kambhamettu C (2013) Can we minimize the influence due to gender and race in age estimation?. In: The 12th International Conference on Machine Learning and Applications(ICMLA 2013), pp 309–314Google Scholar
  22. 22.
    Yun F, Huang TS (2008) Human age estimation with regression on discriminative aging manifold. IEEE Trans Multimed 10(4):578–584CrossRefGoogle Scholar
  23. 23.
    Yan SC, Wang H, Tang X, Huang TS (2007) Learning auto-structured regressor from uncertain nonnegative labels. In: IEEE 11th International Conference on Computer Vision(ICCV 2007), pp 1–8Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.College of Computer Science and Technology of Huaqiao University XiamenXiamenChina

Personalised recommendations