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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 Article
01 Jan 2004
TL;DR: Results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall and demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.
Abstract: In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, In particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.

87 citations

Journal ArticleDOI
TL;DR: How SVD is applied to problems involving image processing is described—in particular, how SVD aids the calculation of so-called eigenfaces, which are an efficient representation of facial images in face recognition.
Abstract: Singular value decomposition (SVD) is one of the most important and useful factoriza- tions in linear algebra. We describe how SVD is applied to problems involving image processing—in particular, how SVD aids the calculation of so-called eigenfaces, which pro- vide an efficient representation of facial images in face recognition. Although the eigenface technique was developed for ordinary grayscale images, the technique is not limited to these images. Imagine an image where the different shades of gray convey the physical three- dimensional structure of a face. Although the eigenface technique can again be applied, the problem is finding the three-dimensional image in the first place. We therefore also show how SVD can be used to reconstruct three-dimensional objects from a two-dimensional video stream.

87 citations

Proceedings ArticleDOI
27 Dec 2005
TL;DR: The essence of 2DPCA is analyzed and a framework of generalized 2D principal component analysis (G2D PCA) is proposed to extend the original 2DpcA in two perspectives: a bilateral-projection-based 2D PCsA (B2DPCS) and a kernel-based 1DPCC (K2D PCs) schemes are introduced.
Abstract: A two-dimensional principal component analysis (2DPCA) by J. Yang et al. (2004) was proposed and the authors have demonstrated its superiority over the conventional principal component analysis (PCA) in face recognition. But the theoretical proof why 2DPCA is better than PCA has not been given until now. In this paper, the essence of 2DPCA is analyzed and a framework of generalized 2D principal component analysis (G2DPCA) is proposed to extend the original 2DPCA in two perspectives: a bilateral-projection-based 2DPCA (B2DPCA) and a kernel-based 2DPCA (K2DPCA) schemes are introduced. Experimental results in face recognition show its excellent performance.

86 citations

Proceedings ArticleDOI
11 Jun 2014
TL;DR: A generalization of the SHADE protocol, called GSHADE, that enables privacy-preserving computation of several distance metrics, including (normalized) Hamming distance, Euclidean distance, Mahalanobis distance, and scalar product.
Abstract: At WAHC'13, Bringer et al. introduced a protocol called SHADE for secure and efficient Hamming distance computation using oblivious transfer only. In this paper, we introduce a generalization of the SHADE protocol, called GSHADE, that enables privacy-preserving computation of several distance metrics, including (normalized) Hamming distance, Euclidean distance, Mahalanobis distance, and scalar product. GSHADE can be used to efficiently compute one-to-many biometric identification for several traits (iris, face, fingerprint) and benefits from recent optimizations of oblivious transfer extensions. GSHADE allows identification against a database of 1000 Eigenfaces in 1.28 seconds and against a database of 10000 IrisCodes in 17.2 seconds which is more than 10 times faster than previous works.

86 citations

Journal ArticleDOI
TL;DR: A methodology for face recognition based on information theory approach of coding and decoding the face image is presented, connection of two stages - Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network.
Abstract: Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave a recognition rate of 97.018% Index Terms—Face recognition, Principal component analysis (PCA), Artificial Neural network (ANN), Eigenvector, Eigenface.

85 citations


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