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Robust Real-Time Face Detection Using Hybrid Neural Networks

  • Ho-Joon Kim
  • Juho Lee
  • Hyun-Seung Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

In this paper, a multi-stage face detection method using hybrid neural networks is presented. The method consists of three stages: preprocessing, feature extraction and pattern classification. We introduce an adaptive filtering technique which is based on a skin-color analysis using fuzzy min-max(FMM) neural networks. A modified convolutional neural network(CNN) is used to extract translation invariant feature maps for face detection. We present an extended version of fuzzy min-max (FMM) neural network which can be used not only for feature analysis but also for pattern classification. Two kinds of relevance factors between features and pattern classes are defined to analyze the saliency of features. These measures can be utilized to select more relevant features for the skin-color filtering process as well as the face detection process.

Keywords

False Alarm Rate Face Detection Convolutional Neural Network Pattern Classifier Feature Range 
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 2006

Authors and Affiliations

  • Ho-Joon Kim
    • 1
  • Juho Lee
    • 2
  • Hyun-Seung Yang
    • 2
  1. 1.School of Computer Science and Electronic EngineeringHandong UniversityPohangKorea
  2. 2.Department of Computer ScienceKAISTDaejeonKorea

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