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  • Research Article
  • Open Access

Cascaded Face Detection Using Neural Network Ensembles

EURASIP Journal on Advances in Signal Processing20072008:736508

  • Received: 6 March 2007
  • Accepted: 8 October 2007
  • Published:


We propose a fast face detector using an efficient architecture based on a hierarchical cascade of neural network ensembles with which we achieve enhanced detection accuracy and efficiency. First, we propose a way to form a neural network ensemble by using a number of neural network classifiers, each of which is specialized in a subregion in the face-pattern space. These classifiers complement each other and, together, perform the detection task. Experimental results show that the proposed neural-network ensembles significantly improve the detection accuracy as compared to traditional neural-network-based techniques. Second, in order to reduce the total computation cost for the face detection, we organize the neural network ensembles in a pruning cascade. In this way, simpler and more efficient ensembles used at earlier stages in the cascade are able to reject a majority of nonface patterns in the image backgrounds, thereby significantly improving the overall detection efficiency while maintaining the detection accuracy. An important advantage of the new architecture is that it has a homogeneous structure so that it is suitable for very efficient implementation using programmable devices. Our proposed approach achieves one of the best detection accuracies in literature with significantly reduced training and detection cost.


  • Detection Efficiency
  • Detection Task
  • Detection Accuracy
  • Face Detection
  • Homogeneous Structure

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Authors’ Affiliations

Philips Research Labs, High Tech Campus 34, Eindhoven, 5656, AE, The Netherlands
Department of Electrical Engineering, Signal Processing Systems (SPS) Group, Eindhoven University of Technology, Eindhoven, Den Dolech2, 5612, AZ, The Netherlands
LogicaCMG, Eindhoven, 5605, JB, The Netherlands


© F. Zuo and P. H. N. deWith 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.