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Effective Face Detection Using a Small Quantity of Training Data

  • Byung-Du Kang
  • Jong-Ho Kim
  • Chi-Young Seong
  • Sang-Kyun Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)

Abstract

We present an effective and real-time face detection method based on Principal Component Analysis (PCA) and Support Vector Machines (SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training dataset in a significant way, so that it works well with a small quantity of training data. It also shows a sufficiently fast detection speed for it to be practical for real-time face detection.

Keywords

Support Vector Machine Face Image Training Image Support Vector Machine Classifier Face Detection 
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

  • Byung-Du Kang
    • 1
  • Jong-Ho Kim
    • 1
  • Chi-Young Seong
    • 1
  • Sang-Kyun Kim
    • 1
  1. 1.Department of Computer ScienceInje UniversityGimhaeKorea

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