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## A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition (2001)

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### Other Repositories/Bibliography

Venue: | In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition |

Citations: | 72 - 11 self |

### Citations

2309 | Eigenfaces vs. fisherfaces: recognition using class specific linear projection
- Belhumeur, Hespanha, et al.
- 1997
(Show Context)
Citation Context ... was an LDA algorithm developed by Zhao and Chellapa [17]. Of the top performing algorithms in FERET, this is the one based upon the oldest and best understood subspace projection technique after PCA =-=[4, 1]-=-. For both these reasons, a similar LDA algorithm has been chosen for our study. Stepping back from face recognition, characterizing the performance of computer vision algorithms has been an ongoing c... |

1379 |
Face recognition using eigenfaces".
- Turk, Pentland
- 1991
(Show Context)
Citation Context ... associated with changes in the test data. As a baseline algorithm, FERET used an Eigenface algorithm that represented the line of classification algorithms based upon PCA that arose from the work by =-=[11, 10]-=-. The PCA algorithm used here is for all intents and purposes equivalent to the Eigenface algorithm used in FERET. One of the top performing algorithms in the FERET evaluation was an LDA algorithm dev... |

1116 | The FERET evaluation methodology for face-recognition algorithms.
- Phillips, Moon, et al.
- 2000
(Show Context)
Citation Context ...e make the source code for the algorithms, scoring procedures and Monte Carlo study available in the hopes others will extend this comparison to newer algorithms. 1. Introduction The FERET evaluation =-=[12]-=- established a common data set and a common testing protocol for evaluating semiautomated and automated face recognition algorithms. It illustrated how much can be accomplished in a well coordinated c... |

728 |
Application of the Karhunen–Loeve procedure for the characterization of human faces
- KirKirbyby, Sirovich
- 1990
(Show Context)
Citation Context ... associated with changes in the test data. As a baseline algorithm, FERET used an Eigenface algorithm that represented the line of classification algorithms based upon PCA that arose from the work by =-=[11, 10]-=-. The PCA algorithm used here is for all intents and purposes equivalent to the Eigenface algorithm used in FERET. One of the top performing algorithms in the FERET evaluation was an LDA algorithm dev... |

424 |
A leisurely look at the bootstrap, the jackknife, and cross-validation,”The
- Efron, Gong
- 1983
(Show Context)
Citation Context ...tem and probe images are novel images to be recognized. The testing data used in this study consists of � images for each of � distinct individuals. Initially, we endeavored to design a bootstrapp=-=ing [5]-=- study, but difficulties described below led us to instead favor permuting probe and gallery choices. Permuting the images selected to serve as gallery and probe generates sample gallery and probe set... |

252 | A Krishnaswamy, "Discriminant Analysis of Principal Components for Face Recognition,"
- Zhao, Chellappa
- 1998
(Show Context)
Citation Context ...e is for all intents and purposes equivalent to the Eigenface algorithm used in FERET. One of the top performing algorithms in the FERET evaluation was an LDA algorithm developed by Zhao and Chellapa =-=[17]-=-. Of the top performing algorithms in FERET, this is the one based upon the oldest and best understood subspace projection technique after PCA [4, 1]. For both these reasons, a similar LDA algorithm h... |

136 | Incremental linear discriminant analysis for face recognition.
- Zhao, Yuen
- 2008
(Show Context)
Citation Context ...ext books often state that Ï is found by solving the general eigenvector problem [4]: Ì (9) Å � Ï �£Å ÏÏ (11) This is true, but provides no insight into why. Nor is it always the best way =-=solve for Ï [18]. We have written -=-a report [8] illustrating the underlying geometry at work and filling out the solution method used in [18]. Projecting an image Ý into LDA subspace yields Ý : Ý � ÏÝ � Ï� � ÝsÜ � (12... |

43 | Performance characterization in computer vision.
- Haralick
- 1994
(Show Context)
Citation Context ...oth these reasons, a similar LDA algorithm has been chosen for our study. Stepping back from face recognition, characterizing the performance of computer vision algorithms has been an ongoing concern =-=[7, 9]-=- and more is certainly being done in this area each year. In comparison, however, far more is written each year about new and different algorithms. See [14, 15] for recent surveys of face recognition ... |

32 | Analysis of PCA-based and Fisher discriminant-based image recognition algorithms.
- Yambor
- 2000
(Show Context)
Citation Context ... such sampling done by brute force retraining on each sample, the computational burden would be staggering. In the past we have studied variation in both PCA and LDA performance subject to retraining =-=[16]-=-. In future we will investigate ways to adapt our methodology efficiently to questions involving changes to the training data. Since it is desirable to have no overlap between training and test data, ... |

28 | Efficient evaluation of classification and recognition systems
- Micheals, Boult
- 2001
(Show Context)
Citation Context ...n written about using modern statistical methods [2] to measure uncertainty in performance measures. 1 http://www.cs.colostate.edu/evalfacerec/ One notable exception is the work by Micheals and Boult =-=[13]-=-. Micheals and Boult use a statistical technique to derive mean and standard deviation estimates for recognition rates at different ranks. They compare a standard PCA algorithm to two algorithms from ... |

26 |
Face Recognition: From Theory to Applications.
- WECHSLER, PHILLIPS, et al.
- 1996
(Show Context)
Citation Context ...on algorithms has been an ongoing concern [7, 9] and more is certainly being done in this area each year. In comparison, however, far more is written each year about new and different algorithms. See =-=[14, 15]-=- for recent surveys of face recognition algorithms. Thus, while the literature on algorithms is vast, little has been written about using modern statistical methods [2] to measure uncertainty in perfo... |

9 | The Geometry of LDA and PCA Classifiers Illustrated with 3D Examples
- Beveridge
- 2001
(Show Context)
Citation Context ... found by solving the general eigenvector problem [4]: Ì (9) Å � Ï �£Å ÏÏ (11) This is true, but provides no insight into why. Nor is it always the best way solve for Ï [18]. We have writt=-=en a report [8] illustrating the -=-underlying geometry at work and filling out the solution method used in [18]. Projecting an image Ý into LDA subspace yields Ý : Ý � ÏÝ � Ï� � ÝsÜ � (12) Training images must be part... |

5 |
Empirical Methods for AI
- Cohen
- 1995
(Show Context)
Citation Context ... different algorithms. See [14, 15] for recent surveys of face recognition algorithms. Thus, while the literature on algorithms is vast, little has been written about using modern statistical methods =-=[2]-=- to measure uncertainty in performance measures. 1 http://www.cs.colostate.edu/evalfacerec/ One notable exception is the work by Micheals and Boult [13]. Micheals and Boult use a statistical technique... |

3 | Face Recognition
- Weng, Swets
- 1998
(Show Context)
Citation Context ...on algorithms has been an ongoing concern [7, 9] and more is certainly being done in this area each year. In comparison, however, far more is written each year about new and different algorithms. See =-=[14, 15]-=- for recent surveys of face recognition algorithms. Thus, while the literature on algorithms is vast, little has been written about using modern statistical methods [2] to measure uncertainty in perfo... |