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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 Article
TL;DR: A combined method for automatic face recognition with both independent component analysis (ICA) and genetic algorithms (GA) was proposed and the fourth order blind identification (FOBI) algorithm was used to derive the independent sources out of the face images.
Abstract: A combined method for automatic face recognition with both independent component analysis (ICA) and genetic algorithms (GA) was proposed. The fourth order blind identification (FOBI) algorithm was used to derive the independent sources out of the face images. To decrease the computing complexity, the dimension of the original image was reduced and GA was applied to the sources set to get an optimal subset of it. Faces can be recognized by classifying the coefficients of the face image projecting to the independent bases. The experiments in the face database show both a higher recognition rate than the eigenface method based on PCA and a lower computing complexity than the traditional ICA face recognition method.

6 citations

Journal ArticleDOI
TL;DR: A new face recognition approach for image feature extraction named two-dimensional parameter principal component analysis (2DPPCA), which achieves better face recognition performance than PCA, 2DPCA, especially on the CMU PIE face database.
Abstract: In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional parameter principal component analysis (2DPPCA). Two-dimensional principal component analysis (2DPCA) is widely used in face recognition. We further study on the basis of 2DPCA. This proposed method is to add a parameter to images samples matrix in the image covariance matrix. Extensive experiments are performed on FERET face database and CMU PIE face database. The 2DPPCA method achieves better face recognition performance than PCA, 2DPCA, especially on the CMU PIE face database.

6 citations

Book ChapterDOI
08 Mar 2011
TL;DR: The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video, which resembles real-life conditions of video surveillance and law-enforcement activities.
Abstract: Recognition of human faces from a distance is highly desirable for law-enforcement. This paper evaluates the use of low-cost, high-definition (HD) body worn video cameras for face recognition from a distance. A comparison of HD vs. Standard-definition (SD) video for face recognition from a distance is presented. HD and SD videos of 20 subjects were acquired in different conditions and at varying distances. The evaluation uses three benchmark algorithms: Eigenfaces, Fisherfaces and Wavelet Transforms. The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video. This scenario resembles real-life conditions of video surveillance and law-enforcement activities. However, at a close range, face data obtained from SD video result in similar, if not better recognition accuracy than using HD face data of the same range.

6 citations

01 Jan 2008
TL;DR: This paper describes an efficient approach for face recognition as a two step process: 1) segmenting the face region from an image by using an appearance based model, 2) using eigenfaces for person identification for segmented face region.
Abstract: This paper describes an efficient approach for face recognition as a two step process: 1) segmenting the face region from an image by using an appearance based model, 2) using eigenfaces for person identification for segmented face region. The efficiency lies not only in generation of appearance models which uses the explicit approach for shape and texture but also the combined use of the aforementioned techniques. The result is an algorithm that is robust against facial expressions variances. Moreover it reduces the amount of texture up to 12% of the image texture instead of considering whole face image. Experiments have been performed on Cohn Kanade facial database using ten subjects for training and sever for testing purposes. This achieved a successful face recognition rate up to 92.85% with and without facial expressions. Face recognition using Principal Component Analysis (PCA) is fast and efficient to use, while the extracted appearance model can be further used for facial recognition and tracking under lighting and pose variations. This combination is simple to model and apply in real time. KeywordsActive shape models, active appearance models, principal components analysis, eigenfaces, face

6 citations

Proceedings ArticleDOI
18 May 2005
TL;DR: A comparative study between standard linear subspace techniques such as eigenfaces and fisherfaces and a novel morphological elastic graph matching for frontal face verification and the experimental results indicate the superiority of the novel Morphological elasticgraph matching against all the other presented techniques.
Abstract: Summary form only given. In this paper, a comparative study between standard linear subspace techniques such as eigenfaces and fisherfaces and a novel morphological elastic graph matching for frontal face verification is presented. A set of experiments has been conducted in the M2VTS database in order to investigate the performance of each algorithm in different image alignment conditions. The experimental results indicate the superiority of the novel morphological elastic graph matching against all the other presented techniques.

6 citations


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