ML Anthology

Learning Representative Local Features for Face Detection

Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhang
CVPR 2001 pp. I:1126-1131
doi:10.1109/CVPR.2001.990657 /cvpr/2001/chen2001cvpr-learning/

Abstract

This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are learnt from the training set instead of arbitrarily defined. The learning procedure consists of two steps. First, a modified version of NMF (non-negative matrix factorization), namely local NMF (LNMF), is applied to obtain an overcomplete set of local features. Second, a learning algorithm based on AdaBoost is used to select a small number of local features and yields extremely efficient classifiers. Experiments are presented which show that face detection performance is comparable to state-of-the-art face detection systems.

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Cite

Text

Chen et al. "Learning Representative Local Features for Face Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990657

Markdown

[Chen et al. "Learning Representative Local Features for Face Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/chen2001cvpr-learning/) doi:10.1109/CVPR.2001.990657

BibTeX

@inproceedings{chen2001cvpr-learning,
  title     = {{Learning Representative Local Features for Face Detection}},
  author    = {Chen, Xiangrong and Gu, Lie and Li, Stan Z. and Zhang, HongJiang},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {I:1126-1131},
  doi       = {10.1109/CVPR.2001.990657},
  url       = {https://mlanthology.org/cvpr/2001/chen2001cvpr-learning/}
}