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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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Book ChapterDOI
20 Dec 2005
TL;DR: The feasibility of the new symbolic PCA method has been successfully tested for face recognition using ORL database and requires less number of features to achieve the same recognition rate.
Abstract: A face recognition technique based on symbolic PCA approach is presented in this paper. The proposed method transforms the face images by extracting knowledge from training samples into symbolic objects, termed as symbolic faces. Symbolic PCA is employed to compute a set of subspace basis vectors for symbolic faces and then to project the symbolic faces into the compressed subspace. New test images are then matched with the images in the database by projecting them onto the basis vectors and finding the nearest symbolic face in the subspace. The feasibility of the new symbolic PCA method has been successfully tested for face recognition using ORL database. As compared to eigenface method, the proposed method requires less number of features to achieve the same recognition rate.

17 citations

Journal ArticleDOI
TL;DR: This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces, which weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face.
Abstract: This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases.

17 citations

Journal ArticleDOI
14 Sep 2006
TL;DR: Experimental results showed that even though the classification accuracy of k-NFP outperforms k-NFL in some dimensions, these rate differences do not have statistical significance.
Abstract: This paper presents an experimental comparison of the nearest feature classifiers, using an approach based on binomial tests in order to evaluate their strengths and weaknesses In addition, classification accuracies and the accuracy-dimensionality tradeoff have been considered as comparison criteria We extend two of the nearest feature classifiers to label the query point by a majority vote of the samples Comparisons were carried out for face recognition using ORL database We apply the eigenface representation for feature extraction Experimental results showed that even though the classification accuracy of k-NFP outperforms k-NFL in some dimensions, these rate differences do not have statistical significance

17 citations

Journal ArticleDOI
01 Jan 2012
TL;DR: The proposed development of face recognition based on a combination of traditional eigenface with artificial neural network (ANN), having the face recognition performance boosted by the classification of discriminant vectors learned from a set of generic samples.
Abstract: The conventional ways of recognizing faces always assume the possession and heavily relies on extensive and representative datasets, but that is not the case in most real-world situations where more often than not, a very limited or even only single sample per person (SSPP) is available which ultimately rendering most face recognition systems to fail severely. This paper proposes a development of face recognition based on a combination of traditional eigenface with artificial neural network (ANN), having the face recognition performance boosted by the classification of discriminant vectors learned from a set of generic samples. The discriminant vectors representing intra-subject and inter-subject variations are learned based on similarities of pairs of generic samples which then used to classify novel intra-subject pairs and inter-subject pairs from probe set and corresponding gallery set. After that, the resulting classification is used to recognize faces by combining it with the expressive ability of eigenface via a voting procedure. The proposed method when tested with FERET and YALE datasets suggests that in face recognition within the SSPP constraints, the performance of the proposed method is better than some state-of-the-art methods.

17 citations

Proceedings ArticleDOI
TL;DR: This paper presents a prototype system that uses facial recognition technology to monitor the authenticated user and demonstrates the feasibility of near-real-time continuous user verification for high-level security information systems.
Abstract: Information security requires a method to establish digital credentials that can reliably identify individual users. Since biometrics is concerned with the measurements of unique human physiological or behavioural characteristics, the technology has been used to verify the identity of computer or network users. Given today's heightened security requirements of military as well as other applications such as banking, health care, etc., it is becoming critical to be able to monitor the presence of the authenticated user throughout a session. This paper presents a prototype system that uses facial recognition technology to monitor the authenticated user. The objective is to ensure that the user who is using the computer is the same person that logged onto the system. A neural network-based algorithm is implemented to carry out face detection, and an eigenface method is employed to perform facial recognition. A graphical user interface (GUI) has been developed which allows the performance of face detection and facial recognition to be monitored at run time. The experimental results demonstrate the feasibility of near-real-time continuous user verification for high-level security information systems.

17 citations


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