ML Anthology

Face Recognition Using Eigenfaces

Matthew A. Turk, Alex Pentland
CVPR 1991 pp. 586-591
doi:10.1109/CVPR.1991.139758 /cvpr/1991/turk1991cvpr-face/

Abstract

An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Turk and Pentland. "Face Recognition Using Eigenfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991. doi:10.1109/CVPR.1991.139758

Markdown

[Turk and Pentland. "Face Recognition Using Eigenfaces." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991.](https://mlanthology.org/cvpr/1991/turk1991cvpr-face/) doi:10.1109/CVPR.1991.139758

BibTeX

@inproceedings{turk1991cvpr-face,
  title     = {{Face Recognition Using Eigenfaces}},
  author    = {Turk, Matthew A. and Pentland, Alex},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1991},
  pages     = {586-591},
  doi       = {10.1109/CVPR.1991.139758},
  url       = {https://mlanthology.org/cvpr/1991/turk1991cvpr-face/}
}