Fast Face Detection Using a Cascade of Neural Network Ensembles

  • Fei Zuo
  • Peter H. N. de With
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3708)


We propose a (near) real-time face detector using a cascade of neural network (NN) ensembles for enhanced detection accuracy and efficiency. First, we form a coordinated NN ensemble by sequentially training a set of neural networks with the same topology. The training implicitly partitions the face space into a number of disjoint regions, and each NN is specialized in a specific sub-region. Second, to reduce the total computation cost for the face detection, a series of NN ensembles are cascaded by increasing complexity of base networks. Simpler NN ensembles are used at earlier stages in the cascade, which are able to reject a majority of non-face patterns in the backgrounds. Our proposed approach achieves up to 94% detection rate on the CMU+MIT test set, a 98% detection rate on a set of video sequences and 3-4 frames/sec. detection speed on a normal PC (P-IV, 3.0GHz).


Detection Accuracy Face Detection Ensemble Classifier False Acceptance Rate Neural Network Ensemble 
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  1. 1.
    Heisele, B., Poggio, T., Pontil, M.: Face detection in still gray images. In: AI Memo, vol. 1687. MIT, Cambridge (2000)Google Scholar
  2. 2.
    Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. PAMI 20(1), 23–28 (1998)Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. Int. Conf. CVPR, vol. 1, pp. 511–518 (2001)Google Scholar
  4. 4.
    Zuo, F., de With, P.H.N.: Fast human face detection using successive face detectors with incremental detection capability. In: Proc. SPIE Electronic Imaging (VCIP 2003), vol. 5022, pp. 831–841 (2003)Google Scholar
  5. 5.
    Duda, R., Hart, P., Stork, D.: Pattern classification, 2nd edn. Wiley interscience, Hoboken (2001) ISBN: 0-471-05669-3zbMATHGoogle Scholar
  6. 6.
    Schneiderman, H., Kanade, T.: A statistical model for 3D object detection applied to faces and cars. In: Proc. Int. Conf. CVPR, vol. 1, pp. 746–751 (2000)Google Scholar
  7. 7.
    Roth, D., Yang, M.-H., Ahuja, N.: A SNoW-based face detector. In: Adv. in NIPS, vol. 12, pp. 855–861. MIT Press, Cambridge (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Fei Zuo
    • 1
  • Peter H. N. de With
    • 1
    • 2
  1. 1.Faculty Electrical EngineeringEindhoven Univ. of TechnologyEindhovenThe Netherlands
  2. 2.LogicaCMGEindhovenThe Netherlands

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