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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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Journal ArticleDOI
TL;DR: Simulation results show that the proposed second-order mixture- of-eigenfaces method is best for face images with illumination variations and the mixture-of- eigenface method isbest for the face imagesWith pose variations in terms of average of the normalized modified retrieval rank and false identification rate.

23 citations

Proceedings ArticleDOI
01 Jan 2017
TL;DR: This paper provides a brief insight of some famous and particularly important algorithms used for face detection like Viola Jones, CNN, Eigenface, Cascade neural networks, etc.
Abstract: This paper provides a brief insight of some famous and particularly important algorithms used for face detection. Face detection is a technology used by/ computer systems to detect faces in a given digital image. Automatic face detection is a very complex problem in image processing and many methods and algorithms have been proposed like Viola Jones, CNN (cascade neural networks), Eigenface etc. We have also tried to talk on each algorithm's efficiency and feasibility.

23 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: The concept of the dasiaaverage-half-facepsila, motivated by the symmetry preserving singular value decomposition, is introduced, and it is shown that the results from the eigenfaces face recognition system using the average- half-face is more accurate than using the full face.
Abstract: We present a promising analysis on using the pattern of symmetry in the face to increase the accuracy of three-dimensional face recognition. We introduce the concept of the dasiaaverage-half-facepsila, motivated by the symmetry preserving singular value decomposition. We compare face recognition results using the eigenfaces face recognition algorithm with average-half-face data and full face data in several experiments on a 3D face data set of 1126 images. We show that the results from the eigenfaces face recognition system using the average-half-face is more accurate than using the full face, only the left or right half of the face or a random choice of half of the face.

23 citations

Journal ArticleDOI
TL;DR: It is proved rigorously that the continuous-time differential equations corresponding to this proposed PCA algorithm will converge to the principal eigenvectors of the autocorrelation matrix of the input signals with the norm of the initial weight vector.

23 citations

Proceedings ArticleDOI
24 Oct 1999
TL;DR: First results for face recognition of video sequences are presented and the main and final objective is to develop a tool to be used in the MPEG-7 standardization effort to help video indexing activities.
Abstract: An integral scheme that provides a global eigen approach to the problem of face recognition of still images has been presented by Lorente and Torres, (1998). The scheme is based on the representation of the face images using the so called eigenfaces, generated performing a PCA (Principal Components Analysis). The data base used was designed for still image recognition and the corresponding images were very controlled. That is, the test images had controlled expression, orientation and lighting variations. Preliminary results were shown using only a frontal view image by person in the training set. In this paper, we present our first results for face recognition of video sequences. To that end, we have modified our original scheme in such a way that is able to cope with the different face conditions present in a video sequence. The main and final objective is to develop a tool to be used in the MPEG-7 standardization effort to help video indexing activities. The system is not yet fully automatic, but an automatic facial point location is under development. Good results have been obtained using the video test sequences used in the MPEG-7 evaluation group.

23 citations


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