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Face Detection with Effective Feature Extraction

  • Sakrapee Paisitkriangkrai
  • Chunhua Shen
  • Jian Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sakrapee Paisitkriangkrai
    • 1
    • 2
  • Chunhua Shen
    • 3
  • Jian Zhang
    • 1
    • 3
  1. 1.The University of New South WalesAustralia
  2. 2.The University of AdelaideAustralia
  3. 3.National ICT AustraliaAustralia

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