ML Anthology

Probabilistic Reasoning Models for Face Recognition

Chengjun Liu, Harry Wechsler
CVPR 1998 pp. 827-832
doi:10.1109/CVPR.1998.698700 /cvpr/1998/liu1998cvpr-probabilistic/

Abstract

We introduce in this paper two probabilistic reasoning models (PPM-1 and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implemented in the reduced PCA subspace. Experiments carried out using 1107 facial images corresponding to 369 subjects (with 169 subjects having duplicate images) from the FERET database show that the PRM approach compares favorably against the two well-known methods for face recognition-the Eigenfaces and Fisherfaces.

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Cite

Text

Liu and Wechsler. "Probabilistic Reasoning Models for Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698700

Markdown

[Liu and Wechsler. "Probabilistic Reasoning Models for Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/liu1998cvpr-probabilistic/) doi:10.1109/CVPR.1998.698700

BibTeX

@inproceedings{liu1998cvpr-probabilistic,
  title     = {{Probabilistic Reasoning Models for Face Recognition}},
  author    = {Liu, Chengjun and Wechsler, Harry},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1998},
  pages     = {827-832},
  doi       = {10.1109/CVPR.1998.698700},
  url       = {https://mlanthology.org/cvpr/1998/liu1998cvpr-probabilistic/}
}