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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)

Abstract

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).

Keywords

Detection Accuracy Face Detection Ensemble Classifier False Acceptance Rate Neural Network Ensemble 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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