Learning discriminant features for multi-view face and eye detection

@article{Wang2005LearningDF,
  title={Learning discriminant features for multi-view face and eye detection},
  author={Peng Wang and Qiang Ji},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  year={2005},
  volume={1},
  pages={373-379 vol. 1}
}
In current face detection, mostly often used features are selected from a large set (e.g. Haar wavelets). Generally Haar wavelets only represent the local geometric feature. When applying those features to profile faces and eyes with irregular geometric patterns, the classifier accuracy is low in the later training stages, only near 50%. In this paper, instead of brute-force searching the large feature set, we propose to statistically learn the discriminant features for object detection… CONTINUE READING
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