Age Estimation Based on Convolutional Neural Network

  • Chenjing Yan
  • Congyan Lang
  • Tao Wang
  • Xuetao Du
  • Chen Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)


In recent years, face recognition technology has become a hot topic in the field of pattern recognition. The human face is one of the most important human biometric characteristics, which contains a lot of important information, such as identity, gender, age, expression, race and so on. Human age is a significant reference for identity discrimination, and age estimation can be potentially applied in human-computer interaction, computer vision and business intelligence. This paper addresses the problem of accurate estimation of human age. An age estimation system is generally composed of aging feature extraction and feature classification. In the feature extraction part, well-known texture descriptors like the Gabor wavelets and the Local Binary Patterns (LBP) have been utilized for the feature extraction. In our method, we use Convolutional Neural Network (CNN) to extract facial features. We gain the convolution activation features through building a multilevel CNN model based-on abundant training data. In the feature classification part, we divide different ages into 13 groups and use the Support Vector Machine (SVM) classifier to perform the classification. The experimental results show that the performance of the proposed method is superior to that of the previous methods when using our aging database.


age estimation convolutional neural network SVM feature extraction classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(1), 621–628 (2004)CrossRefGoogle Scholar
  2. 2.
    Zhao, W.Y., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computer Surveys 35(4), 399–459 (2003)CrossRefGoogle Scholar
  3. 3.
    Chao, W.L., Liu, J.Z., Ding, J.J.: Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognition 46(3), 628–641 (2013)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Kwon, Y.H., Lobo, N.V.: Age classification from facial images. Computer Vis. Image Understand. 74(1), 1–21 (1999)CrossRefGoogle Scholar
  6. 6.
    Geng, X., Zhou, Z.H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12), 2234–2240 (2007)CrossRefGoogle Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image Net Classification with Deep Convolutional Neural Networks. In: NIPS, vol. 1(2), p. 4 (2012)Google Scholar
  8. 8.
    Hayashi, J., Yasumoto, M., Ito, H., et al.: Age and gender estimation based on wrinkle texture and color of facial images. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 1 (2002)Google Scholar
  9. 9.
    Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE TPAMI (2001)Google Scholar
  10. 10.
    Yang, Z.G., Ai, H.Z.: Demographic classification with local binary patterns. In: Proc. of International Conference on Biometrics, pp. 464–473 (2007)Google Scholar
  11. 11.
    Guo, G.D., Mu, G.W., Fu, Y., Huang, T.S.: Human age estimation using bio-inspired features. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2009), pp. 112–119 (2009)Google Scholar
  12. 12.
    Choi, S.E., Lee, Y.J., Lee, S.J., et al.: Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recognition 44(6), 1262–1281 (2011)CrossRefzbMATHGoogle Scholar
  13. 13.
    Donahue, J., Jia, Y.Q., Vinyals, O., et al.: Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310-1531 (2013)Google Scholar
  14. 14.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 24(4), 442–455 (2002)CrossRefGoogle Scholar
  15. 15.
    Ni, B., Song, Z., Yan, S.: Web image mining towards universal age estimator. In: ACM MM (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chenjing Yan
    • 1
  • Congyan Lang
    • 1
  • Tao Wang
    • 1
  • Xuetao Du
    • 2
  • Chen Zhang
    • 2
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityChina
  2. 2.China Mobile Group Design Institute Co., Ltd.BeijingChina

Personalised recommendations