A Novel Face Detection Method Based on Contourlet Features

  • Huan Yang
  • Yi Liu
  • Tao Sun
  • Yongmi Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5754)


This paper primarily investigates a novel face detection method based on contourlet features. In this method, a face-pyramid is developed through contourlet transform, which includes both low and high frequency information to represent face features on multiresolutions and multidirections. The most discriminative features are then selected from the face-pyramid and are trained to construct the classifier by using the cascade boosting algorithm (Adaboost). Speed and capability are important issues for current face detection systems. This method extensively reduces feature demensions and the negative sample numbers step by step, so that the speed is increased radically. Mean-face template matching is adopted finally in the system to ensure a detection of one face in a scanned image. Extensive experiments are conducted and the results show that the proposed method is efficient in detecting frontal faces from cluttered images.


Contourlet transform Face-pyramid Adaboost Template matching 


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  1. 1.
    Yang, M.H., Kriegman, D., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Tr. Pattern Analysis and Machine Intelligence 24, 34–58 (2002)CrossRefGoogle Scholar
  2. 2.
    Erik, H., Boon, K.L.: Face Detection: A Survey. Computer Vision and Image Understanding, 236–274 (2001)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  4. 4.
    Viola, P., Jones, M.: Robust Real-Time Face Detection. In: Conf. International Journal of Computer Vision, pp. 137–154 (2004)Google Scholar
  5. 5.
    Li, S.Z., Zhu, L., Zhang, Z.Q., Zhang, H.J.: Learning to detect multi-view faces in real-time. In: Proc. Development and Learning, Proceedings, pp. 172–177 (2002)Google Scholar
  6. 6.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Viola, P.: Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade. In: Advances in Neural Information Processing Systems, pp. 1311–1318 (2001)Google Scholar
  8. 8.
    Randazzo, V., Usai, L.: An Improvement of AdaBoost for Face-Detection with Motion and Color Information. In: Conf. Image Analysis and Processing, ICIAP 2007 (2007)Google Scholar
  9. 9.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: International Conference on Proc. Image Processing, vol. 901, pp. 900–903 (2002)Google Scholar
  10. 10.
    Yan, Y.Y., Guo, Z.B.: Multi-view Face Detection Based on the Enhanced AdaBoost Using Walsh Features. In: Proc. Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, vol. 1, pp. 200–205 (2007)Google Scholar
  11. 11.
    Lin-Lin, H., Shimizu, A.: Classification-Based Face Detection Using Gabor Filter Features. In: Conf. Automatic Face and Gesture Recognition, pp. 397–402 (2004)Google Scholar
  12. 12.
    Chen, J., Shan, S.G.: Novel face detection method based on gabor features. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 90–99. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Zhang, J.L., Zhang, Z.Y.: Face Recognition Based on Curvefaces. In: Third Intelnational Conference on Natural Computation (ICNC 2007), vol. 2, pp. 627–631 (2007)Google Scholar
  14. 14.
    Minh, N., Martin, V.: The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Transactions on Image Processing 14 (2005)Google Scholar
  15. 15.
    Duncan, D., Po, Y., Minh, N.D.: Directional Multiscale Modeling of Images Using the Contourlet Transform. IEEE Transactions on Image Processing 15, 1610–1620 (2006)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Li, B.B., Li, X.: A Multiscale and Multidirectional Image Denoising Algorithm Based on Contourlet Transform. In: 2006 IEEE Conf. on Intelligent Information Hiding and Multimedia Signal Processing (2006)Google Scholar
  17. 17.
    Dai, S.W.: Image Denoising Based on Complex Contourlet Transform. In: Conf. on Wavelet Analysis and Pattern Recognition (ICWAPR 2007), vol. 4, pp. 1742–1747 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Huan Yang
    • 1
  • Yi Liu
    • 1
  • Tao Sun
    • 1
  • Yongmi Yang
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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