Global Haar-Like Features: A New Extension of Classic Haar Features for Efficient Face Detection in Noisy Images

  • Mahdi Rezaei
  • Hossein Ziaei Nafchi
  • Sandino Morales
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)


This paper addresses the problem of detecting human faces in noisy images. We propose a method that includes a denoising preprocessing step, and a new face detection approach based on a novel extension of Haar-like features. Preprocessing of the input images is focused on the removal of different types of noise while preserving the phase data. For the face detection process, we introduce the concept of global and dynamic global Haar-like features, which are complementary to the well known classical Haar-like features. Matching dynamic global Haar-like features is faster than that of the traditional approach. Also, it does not increase the computational burden in the learning process. Experimental results obtained using images from the MIT-CMU dataset are promising in terms of detection rate and the false alarm rate in comparison with other competing algorithms.


Face detection Global Haar-like features Phase-preserving denoising AdaBoost 


  1. 1.
    Hou, X., Liu, C.L., Tan, T.: Learning boosted asymmetric classifiers for object detection. In: Computer Vision and Pattern Recognition, pp. 330–338 (2006)Google Scholar
  2. 2.
    Kovesi, P.: Phase Preserving Denoising of Images. Digital Image Computing, Techniques and Applications (1999)Google Scholar
  3. 3.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)Google Scholar
  4. 4.
    Lee, K.C., Ho, J., Kreigman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Analysis Machine Intelligence 27, 684–698 (2005)CrossRefGoogle Scholar
  5. 5.
    Li, S.Z., Zhang, Z.: Floatboost learning and statistical face detection. IEEE Trans. PAMI 26, 1112–1123 (2004)CrossRefGoogle Scholar
  6. 6.
    Lienthart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: International Conference on Image Processing, pp. 900–903 (2002)Google Scholar
  7. 7.
    Liu, C., Shum, H.Y.: Kullback-leibler boosting. In: Computer Vision and Pattern Recognition, pp. 587–594 (2003)Google Scholar
  8. 8.
    Martinez, A.M., Benavente, R.: The A.R. face dataset. CVC Technical Report (1998)Google Scholar
  9. 9.
    Masnadi Shirazi, H., Vasconcelos, N.: Cost-Sensitive Boosting. IEEE Trans. PAMI 33, 294–309 (2011)CrossRefGoogle Scholar
  10. 10.
    Masnadi Shirazi, H., Vasconcelos, N.: High Detection-rate Cascades for Real-Time Object Detection. In: ICCV, pp. 1–6 (2007)Google Scholar
  11. 11.
    Pham, M.T., Cham, T.J.: Fast Training and Selection of Haar features using Statistics in Boosting-based Face Detection. In: International Conference on Computer Vision, pp. 1–7 (2007)Google Scholar
  12. 12.
    Pham, M.T., Gao, Y., Houng, V.T.D., Cham, T.J.: Fast Polygonal Integration and Its Application in Extending Haar-like Features to Improve Object Detection. In: Computer Vision and Pattern Recognition, pp. 942–949 (2010)Google Scholar
  13. 13.
    Rezaei, M., Klette, R.: Novel Adaptive Eye Detection and Tracking for Challenging Lighting Conditions. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part II. LNCS, vol. 7729, pp. 427–440. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Rezaei, M., Terauchi, M.: Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory. In: 6th Pacific-Rim Symposium on Image and Video Technology (2013)Google Scholar
  15. 15.
    Saberian, M.J., Vasconcelos, N.: Learning Optimal Embedded Cascades. IEEE Trans. PAMI 34, 2005–2018 (2012)CrossRefGoogle Scholar
  16. 16.
    Saberian, M.J., Vasconcelos, N.: Boosting Classifier Cascades. In: Neural Information Processing Systems (2010)Google Scholar
  17. 17.
    Struc, V., Vesnicer, B., Mihelic, F., Pavesic, N.: Removing illumination artifacts from face images using the nuisance attribute projection. In: IEEE International Conference on Acoustics Speech and Signal Processing, pp. 846–849 (2011)Google Scholar
  18. 18.
    Vaudrey, T., Morales, S., Wedel, A., Klette, R.: Generalized Residual Images Effect on Illumination Artifact Removal for Correspondence Algorithms. Pattern Recognition 44, 2034–2046 (2011)CrossRefGoogle Scholar
  19. 19.
    Viola, P., Jones, M.: Robust Real-Time Face Detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  20. 20.
    Xiao, R., Zhu, L., Zhang, H.J.: Boosting chain learning for object detection. In: Computer Vision and Pattern Recognition, pp. 709–715 (2003)Google Scholar
  21. 21.
    Yang, M.H., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. PAMI 24, 34–58 (2002)CrossRefGoogle Scholar
  22. 22.
    Zhang. C., Zhang, Z.: Boosting-Based Face Detection and Adaptation. Morgan & Claypool (2010)Google Scholar
  23. 23.
    Zhang, C., Zhang, Z.: A Survey of Recent Advances in Face Detection. Microsoft Research. Technical Report, MSR-TR-2010-66 (2010)Google Scholar
  24. 24.
    Carnegie Mellon University image dataset,

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Hossein Ziaei Nafchi
    • 2
  • Sandino Morales
    • 1
  1. 1.The University of AucklandNew Zealand
  2. 2.Synchromedia LaboratoryÉcole de Technologie SupérieureCanada

Personalised recommendations