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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 2007
TL;DR: In this article, a person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved.
Abstract: Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual’s face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject’s face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the “hallucination” algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

7 citations

Journal Article
TL;DR: It was found that the probability density function of the distance between the projection vector of the input face image and the average projection vectors of the subject in the face database, follows Rayleigh distribution.
Abstract: A new approach is adopted in this paper based on Turk and Pentland’s eigenface method. It was found that the probability density function of the distance between the projection vector of the input face image and the average projection vector of the subject in the face database, follows Rayleigh distribution. In order to decrease the false acceptance rate and increase the recognition rate, the input face image has been recognized using two thresholds including the acceptance threshold and the rejection threshold. We also find out that the value of two thresholds will be close to each other as number of trials increases. During the training, in order to reduce the number of trials, the projection vectors for each subject has been averaged. The recognition experiments using the proposed algorithm show that the recognition rate achieves to 92.875% whilst the average number of judgment is only 2.56 times. Keywords—Eigenface, Face Recognition, Threshold, Rayleigh Distribution, Feature Extraction

7 citations

Proceedings ArticleDOI
21 May 2012
TL;DR: This paper mainly researches the method of face recognition based on PCA and K-Nearest Neighbor and a comparison of NearestNeighbor and Nearest Neighbor are given in the last section.
Abstract: Principle Component Analysis (PCA) technique is of vital importance and is an efficient method in extracting features in face recognition. Generally speaking, the image always needs to be transformed into ID vector in PCA. This paper mainly researches the method of face recognition based on PCA and K-Nearest Neighbor. A comparison of Nearest Neighbor and K-Nearest Neighbor are given in the last section.

7 citations

Proceedings ArticleDOI
29 Jul 2017
TL;DR: A new method to collect face data which can get lots of face data quickly and conveniently is designed which can be achieved through the Convolutional Neural Network.
Abstract: Conventionally, students attendance records are taken manually by teachers through roll calling in the class. It is time-consuming and prone to errors. Moreover, records of attendance are difficult to handle and preserve for the long-term. In this paper, we propose a more conveniently method of attendance statistics, which achieved through the Convolutional Neural Network (CNN). The traditional method of face recognition, such as Eigenface, is sensitive to lighting, noise, gestures, expressions and etc. Hence, we utilize CNN to implement face recognition, in order to reduce the effect of environmental change on experimental results. In addition, CNN is a method which needs lots of data for training. To resolve the problem, we design a new method to collect face data which can get lots of face data quickly and conveniently.

7 citations

Proceedings ArticleDOI
01 Jan 2007
TL;DR: In this article, a simple implementation of face recognition using principal component analysis based on information theory concepts, seek a computational model that best describes a face, by extracting the most relevant information contained in that face.
Abstract: This paper gives the simple implementation of face recognition using principal component analysis, based on information theory concepts, seek a computational model that best describes a face, by extracting the most relevant information contained in that face. This technique is not new, but in this paper the implementation is given with very simple manner. Eigenfaces approach is a principal component analysis method, in which a small set of characteristic pictures are used to describe the variation between face images. A face recognition system, based on the eigenfaces approach is proposed. Eigenfaces approach seems to be an adequate method to be used in face recognition due to its simplicity, speed and learning capability. Experimental results using MATLAB are presented in this paper to demonstrate the viability of the proposed face recognition method. The face orientation is also considered in this experiment, and gives very good results.

7 citations


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