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Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.


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Proceedings ArticleDOI
07 Oct 2011
TL;DR: This paper presents performance comparison of three leading face recognition techniques, which combine K-nearest neighbor (KNN) classification method with PCA and principle component analysis (PCA).
Abstract: This paper presents performance comparison of three leading face recognition techniques. In the first method, the face recognition is done using principle component analysis (PCA). In the second method we combine K-nearest neighbor (KNN) classification method with PCA. The face recognition using histogram is also carried out. The above methods are compared on the basis of accuracy and time taken in an ORL database and YALE database.

9 citations

01 Jan 2014
TL;DR: It is reported that the highest recognition rate is equally achieved by MATLAB's eigenvalue method and Hotelling's de∞ation, and the former is observed to be the fastest for large numbers of dominant eigenfaces while scaling the best with the number of computational cores.
Abstract: We compare four commonly used eigenvector methods, namely cyclic Jacobi's method of iteration, Wiedlandt's de∞ation, Hotelling's de∞ation, and MATLAB's own eigen- value method for the success of face recognition which is based on Principal Component Analysis (PCA). We report that the highest recognition rate is equally achieved by MATLAB's eigenvalue method and Hotelling's de∞ation. The former is observed to be the fastest for large numbers of dominant eigenfaces while scaling the best with the number of computational cores. On the other hand, the latter has a brief and open source code that can be easily modifled for a given purpose. We further investigate the impact of altering face images to improve the recognition rate. Difierent sets of images have been obtained from two well-known face databases, various efiects using imaging fllters have been applied to them, and the resulting sets have been used as both training and test sets. Recognition rates reveal that some of these flltered sets can be even better candidates for training and testing than the original sets.

9 citations

Book ChapterDOI
30 Nov 2004
TL;DR: A novel Gabor-Kernel Fisher analysis method is proposed, which applies Enhanced Kernel Fisher Model (EKFM) on Gaborfaces derived from Gabor wavelet representation of face images, and it is shown that the EKFM outperforms the Generalized Kernel Fisher Analysis (GKFD) model.
Abstract: Kernel based methods have been of wide concern in the field of machine learning. This paper proposes a novel Gabor-Kernel Fisher analysis method (G-EKFM) for face recognition, which applies Enhanced Kernel Fisher Model (EKFM) on Gaborfaces derived from Gabor wavelet representation of face images. We show that the EKFM outperforms the Generalized Kernel Fisher Analysis (GKFD) model. The performance of G-EKFM is evaluated on a subset of FERET database and CAS-PEAL database by comparing with various face recognition schemes, such as Eigenface, GKFA, Image-based EKFM, Gabor-based GKFA, and so on.

9 citations

Proceedings ArticleDOI
13 Jul 2001
TL;DR: A robust algorithm for automatically learning an appearance subspace of objects performing rigid motion through an image sequence, given a manual initialization of the regions of support (masks) in the first frame is described.
Abstract: This paper describes a robust algorithm for automatically learning an appearance subspace of objects performing rigid motion through an image sequence, given a manual initialization of the regions of support (masks) in the first frame. The learning process is posed as a continuous optimization problem and it is solved with a mixture of stochastic and deterministic techniques achieving sub-pixel accuracy. Additionally, we learn the dynamics of the motion and appearance parameters for scene characterization and point out the benefits of working with modular eigenspaces. Preliminary results of automatic learning a modular eigenface model with applications to real time video conferencing, human computer interaction and actor animation are reported.

9 citations

Book ChapterDOI
06 Jun 2007
TL;DR: The experimental results show that the proposed quaternion representation of a color image for face recognition gives a very significant improvement when compared to using only the illuminance information.
Abstract: Color has plenty of discriminative information that can be used to improve the performance of face recognition algorithms, although it is difficult to use it because of its high variability. In this paper we investigate the use of the quaternion representation of a color image for face recognition. We also propose a new representation for color images based on complex numbers. These two color representation methods are compared with the traditional grayscale and RGB representations using an eigenfaces based algorithm for identity verification. The experimental results show that the proposed method gives a very significant improvement when compared to using only the illuminance information.

9 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202316
202249
202120
202043
201953
201840