Eigenfaces for Recognition

@article{Turk1991EigenfacesFR,
  title={Eigenfaces for Recognition},
  author={Matthew A. Turk and Alex Pentland},
  journal={Journal of Cognitive Neuroscience},
  year={1991},
  volume={3},
  pages={71-86}
}
We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem… 
Face recognition using eigenfaces
  • M. Turk, A. Pentland
  • Computer Science
    Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1991
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
Face Recognition Machine Vision System Using Eigenfaces
TLDR
An approach to detect and identification of human faces which is real time face recognition system is developed with Graphical User Interface (GUI) based on an information theory approach that decomposes face images into a small set of characteristics feature images called "eigenfaces".
Interactive-time vision: face recognition as a visual behavior
TLDR
A near-real-time computer system which locates and tracks a subject's head and then recognize the person by comparing characteristics of the face to those of known individuals, and provides for the ability to learn and later recognize new faces in an unsupervised manner.
Eigenface and PCA Based Face Recognition System
TLDR
The feature of PCA as a mechanism for extracting facial features is dealt with, where the principal components are computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.
FACE RECOGNITION USING EIGENFACE APPROACH
TLDR
The Eigenface approach uses Principal Component Analysis (PCA) algorithm for the recognition of the images and gives an efficient way to find the lower dimensional space.
A Real-Time Face Recognition System Using Eigenfaces
TLDR
The proposed approach essentially was to implement and verify the algorithm Eigenfaces for Recognition, which solves the recognition problem for two dimensional representations of faces, using the principal component analysis.
Face detection and recognition using PCA
  • Sangjean Lee, S. Jung, J. Kwon, Seung-Hong Hong
  • Computer Science
    Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030)
  • 1999
TLDR
A computer system that can locate and track a subject's head in a complex background and then recognize the person by comparing characteristics of the face to those of known individuals is developed.
Eigenfaces and Beyond
Project 1: Face Recognition
TLDR
A top-down system using a statistical approach to classify and match facial parameters, and the use of deformable templates for face recognition in terms of parametrized geometrical models are developed.
International Journal of Recent Trends in Engineering , Vol 2 , No . 2 , November 2009 1
TLDR
An approach to detect and identification of human faces which is real time face recognition system is developed with Graphical User Interface (GUI), which takes a snap of a person from the camera and store at a predefined place, which is treated as a two-dimensional face recognition problem.
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