Real-Time Face Detection Using Illumination Invariant Features

  • Klaus Kollreider
  • Hartwig Fronthaler
  • Josef Bigun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


A robust object/face detection technique processing every frame in real-time (video-rate) is presented. A methodological novelty are the suggested quantized angle features (“quangles”), being designed for illumination invariance without the need for pre-processing, e.g. histogram equalization. This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. Separable filtering and the use of lookup tables favor the detection speed. Furthermore, the gradient may then be reused for other tasks as well. A side effect is that the training of effective cascaded classifiers is feasible in very short time, less than 1 hour for data sets of order 104. We present favorable results on face detection, for several public databases (e.g. 93% Detection Rate at 1×10− 6 False Positive Rate on the CMU-MIT frontal face test set).


Object detection Face Detection Biometrics Direction Field Orientation Tensor Quantized Angles Quangles AdaBoost 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Klaus Kollreider
    • 1
  • Hartwig Fronthaler
    • 1
  • Josef Bigun
    • 1
  1. 1.Halmstad University, Box 823, SE-30118, HalmstadSweden

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