Live Stream Oriented Age and Gender Estimation using Boosted LBP Histograms Comparisons

Lionel Prevost, Philippe Phothisane, Erwan Bigorgne


Research has recently focused on human age and gender estimation because they are useful cues in many applications such as human-machine interaction, soft biometrics or demographic statistics for marketing. Even though human perception of other people’s age is often biased, attaining this kind of precision with an automatic estimator is still a difficult challenge. In this paper, we propose a real time face tracking framework that includes a sequential estimation of people’s gender then age. A single gender estimator and several gender-specific age estimators are trained using a boosting scheme and their decisions are combined to output a gender and an age in years. We choose to train all these estimators using local binary patterns histograms extracted from still facial images. The whole process is thoroughly tested on state-of art databases and video sets. Results on the popular FG-NET database show results comparable to human perception (overall 70% correct responses within 5 years tolerance and almost 90% within 10 years tolerance). The age and gender estimators can output decisions at 21 frames per second. Combined with the face tracker, they provide real-time estimations of age and gender.


  1. Anastasi, J. and Rhodes, M. (2005). An own-age bias in face recognition for children and older adults. Psy. Bul. & Rev., 12(6):1043-1047.
  2. Baluja, S. and Rowley, H. (2007). Boosting sex identification performance. IJCV, 71:111-119.
  3. Cottrell, G. and Metclafe, J. (1990). Empath: face, emotion, and gender recognition using holons. Proc. of Adv. in NIPS, 3:567-571.
  4. Freund, Y. and Schapire, H. (1996). Experiments with a new boosting algorithm. In Int. Conf. on Machine Learning, pages 148-156.
  5. Fu, Y., Guo, G., and Huang, T. (2010). Age synthesis and estimation via faces: A survey. IEEE Trans. on PAMI, 32(11):1955-1976.
  6. Gao, F. and Ai, H. (2009). Face age classification on consumer images with gabor feature and fuzzy lda method. LNCS, 5558:132-141.
  7. Golomb, B., Lawrence, D., and Sejnowski, T. (1991). Sexnet, a neural network identifies sex from human faces. NIPS, 3.
  8. Guo, G., Mu, G., Dyer, D., and T.S., H. (2009a). A study on automatic age estimation using a large database. ICCV, pages 1986-1991.
  9. Guo, G., Mu, G., Fu, Y., and Huang, T. (2009b). Human age estimation using bio inspired features. CVPR, pages 112-119.
  10. Hadid, A. and M., P. (2008). Combining motion and appearance for gender classification from video sequences. ICPR, pages 1-4.
  11. Han, H., Otto, C., and Jain, A. (2013). Age estimation from face images: Human vs. machine performance. In Proc. ICB, pages 4-7.
  12. Huang, G., Ramesh, M., Berg, T., and Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Uni. of Massachusetts, Tech. Report 07-49.
  13. Kumar, N., Belhumeur, P., and Nayar, S. (2008). Facetracer: A search engine for large collections of images with faces. ECCV, pages 340-353.
  14. Lanitis, A., Draganova, C., and Christodoulou, C. (2004). Comparing different classiers for automatic age estimation. IEEE Trans. on SMC-B, 34(1):621-628.
  15. Li, X., Maybank, S., Yan, S., Tao, D., and D., X. (2008). Gait components and their application to gender recognition. IEEE Trans. on SMC-C, 38(2):145- 155.
  16. Liao, S., Zhu, X., Lei, Z., Zhang, L., and Li, S. (2007). Learning multi-scale block local binary patterns for face recognition. ICB, pages 828-837.
  17. Luu, K., Ricanek, K., Bui, T., and Suen, C. (2009). Age estimation using active appearance models and support vector machine regression. the IEEE Conf. on Biometrics: Theory, Applications, and Systems (BTAS), pages 1-5.
  18. Luu, K., Seshadri, K., Savvides, M., and Bui, T.D.and Suen, C. (2011). Contourlet appearance model for facial age estimation. Int. Joint Conf. on Biometrics (IJCB), pages 1-8.
  19. Makinen, E. and Raisamo, R. (2008). An experimental comparison of gender classification methods. Pattern Recognition Letters, 29(10):1544-1556.
  20. Moghaddam, B. and Yang, M. (2002). Learning gender with support faces. IEEE trans. on PAMI, 24(5):707- 711.
  21. Ojala, T., Pietikinen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on PAMI, 24:7:971-9.
  22. Phothisane, P., Bigorgne, E., Collot, L., and Prevost, L. (2011). A robust composite metric for head pose tracking using an accurate face model. In Proc. FG, pages 694-699.
  23. Ramanathan, N. and Chellappa, R. (2006). Face verification across age progression. IEEE Trans. on Image Processing, 15(11):3349-3361.
  24. Shakhnarovich, G., Viola, P., and Moghaddam, B. (2002). A unified learning framework for real time face detection and classification. FG, pages 14-21.
  25. Shan, C. (2012). Learning local binary patterns for gender classification on real-world face images. Patt. Recog. Letters, 33(4):431-437.
  26. Shan, C., Gong, S., and McOwan, P. (2008). Fusing gait and face cues for human gender recognition. NVR, 71(10-12):1931-1938.
  27. Thukral, P., Mitra, K., and Chellappa, R. (2012). A hierarchical approach for human age estimation. ICASSP, pages 1529-1532.
  28. Wild, H., Barett, S., Spence, M. J., O'Toole, A., Cheng, Y., and Brooke, J. (2000). Recognition and sex categorization of adults' and children's faces: Examining performance in the absence of sex-stereotyped cues. Jour. of Exp. Child Psychology, 77:269-291.
  29. Yan, S., Wang, H., Tang, X., and T.S., H. (2007). Learning auto-structured regressor from uncertain nonnegative labels. ICCV, pages 1-8.
  30. Zhang, W., Shan, S., Zhang, H., Gao, W., and Chen, X. (2005). Multi-resolution histograms of local variation patterns (mhlvp) for robust face recognition. Audio- and Video-Based Biometric Person Authentication, pages 7:937-944.

Paper Citation

in Harvard Style

Prevost L., Phothisane P. and Bigorgne E. (2014). Live Stream Oriented Age and Gender Estimation using Boosted LBP Histograms Comparisons . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 790-798. DOI: 10.5220/0004927207900798

in Bibtex Style

author={Lionel Prevost and Philippe Phothisane and Erwan Bigorgne},
title={Live Stream Oriented Age and Gender Estimation using Boosted LBP Histograms Comparisons},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Live Stream Oriented Age and Gender Estimation using Boosted LBP Histograms Comparisons
SN - 978-989-758-018-5
AU - Prevost L.
AU - Phothisane P.
AU - Bigorgne E.
PY - 2014
SP - 790
EP - 798
DO - 10.5220/0004927207900798