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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
21 Jun 2016
TL;DR: A new approach for identity recognition using rank-level fusion of multiple face representations based on fusion of two well-known appearance-based techniques, Principal Component Analysis and Linear Discriminant Analysis is presented.
Abstract: Face recognition has become one of the most successful applications in the field of image analysis and understanding. This paper presents a new approach for identity recognition using rank-level fusion of multiple face representations. In this paper, we propose face recognition based on fusion of two well-known appearance-based techniques, Principal Component Analysis and Linear Discriminant Analysis. Fusion is done at rank level using Borda count method. Our experimental work demonstrates significant improvement in recognition accuracy over individual face representations.

27 citations

Journal ArticleDOI
TL;DR: A novel technique aimed to make full use of the color cues is proposed to improve the accuracy of color face recognition based on principal component analysis and can achieve higher accuracy than regular PCA methods.

27 citations

Proceedings ArticleDOI
11 May 2005
TL;DR: It is shown that modular PCA improves the accuracy of face recognition when the face images have varying expression and illumination, and the flexible and parallel architecture design consists of multiple processing elements to operate on predefined regions of a face image.
Abstract: We describe a flexible and efficient multilane architecture for real-time face recognition system based on modular principal component analysis (PCA) method in a field programmable gate array (FPGA) environment. We have shown in Gottumukkal R., and Asan K.V., (2004) that modular PCA improves the accuracy of face recognition when the face images have varying expression and illumination. The flexible and parallel architecture design consists of multiple processing elements to operate on predefined regions of a face image. Each processing element is also parallelized with multiple pipelined paths/lanes to simultaneously compute weight vectors of the non-overlapping region, hence called multilane architecture. The architecture is able to recognize a face image from a database of 1000 face images in 11ms.

27 citations

Proceedings ArticleDOI
20 Aug 2006
TL;DR: This work integrates the Fisher linear discriminant analysis into the NMF algorithm, which results in a novel modified non-negative matrix factorization algorithm that guarantees the non-negativity for all the coefficients and hence preserve the intuitive meaning for the base vectors and weight vectors while facilitating the supervised learning of within-class information.
Abstract: In this paper, we propose a new variation of the non-negative matrix factorization (NMF) for face recognition. The original NMF algorithm is distinguished from the other methods of pattern recognition by its non-negativity constraints which lead to a parts-based representation because they allow only additive combinations. However, it should be considered as an unsupervised method since class information in the training set is not used. To take advantage of more information in the training images and improve the performance for classification problem, we integrate the Fisher linear discriminant analysis into the NMF algorithm, which results in a novel modified non-negative matrix factorization algorithm. Our new update rule guarantees the non-negativity for all the coefficients and hence preserve the intuitive meaning for the base vectors and weight vectors while facilitating the supervised learning of within-class information. Our new technique is tested on a well-known face database: the ORL Face Database. The experimental results are very encouraging and outperformed traditional techniques including the original NMF and the eigenface method

26 citations

Book ChapterDOI
11 Apr 2000
TL;DR: The main contributions of the present paper are the description of the performance assessment framework (which is still under development), the results of the two experiments and a discussion of some possible reasons for them.
Abstract: The Principal Components Analysis (PCA) is one of the most successfull techniques that have been used to recognize faces in images. This technique consists of extracting the eigenvectors and eigenvalues of an image from a covariance matrix, which is constructed from an image database. These eigenvectors and eigenvalues are used for image classification, obtaining nice results as far as face recognition is concerned. However, the high computational cost is a major problem of this technique, mainly when real-time applications are involved. There are some evidences that the performance of a PCA-based system that uses only the region around the eyes as input is very close to a system that uses the whole face. In this case, it is possible to implement faster PCA-based face recognition systems, because only a small region of the image is considered. This paper reports some results that corroborate this thesis, which have been obtained within the context of an ongoing project for the development of a performance assessment framework for face recognition systems. The results of two PCA-based recognition experiments are reported: the first one considers a more complete face region (from the eyebrows to the chin), while the second is a sub-region of the first, containing only the eyes. The main contributions of the present paper are the description of the performance assessment framework (which is still under development), the results of the two experiments and a discussion of some possible reasons for them.

26 citations


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