Topic
Eigenface
About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.
Papers published on a yearly basis
Papers
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23 Aug 2004TL;DR: This paper performs principal component analysis in the frequency domain on the phase spectrum of the face images and improves the recognition performance in the presence of illumination variations dramatically compared to normal eigenface method and other competing face recognition methods such as the illumination subspace method and fisherfaces.
Abstract: In this paper, we present a novel method for performing robust illumination-tolerant and partial face recognition that is based on modeling the phase spectrum of face images. We perform principal component analysis in the frequency domain on the phase spectrum of the face images and we show that this improves the recognition performance in the presence of illumination variations dramatically compared to normal eigenface method and other competing face recognition methods such as the illumination subspace method and fisherfaces. We show that this method is robustly even when presented with partial views of the test faces, without performing any pre-processing and without needing any a-priori knowledge of the type or part of face that is occluded or missing. We show comparative results using the illumination subset of CMU-PIE database consisting of 65 people showing the performance gain of our proposed method using a variety of training scenarios using as little as three training images per person. We also present partial face recognition results that obtained by synthetically blocking parts of the face of the test faces (even though training was performed on the full face images) showing gain in recognition accuracy of our proposed method.
98 citations
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23 Jun 1998TL;DR: This paper proposes a novel pattern classification approach, called the nearest linear combination (NLC) approach, for eigenface based face recognition, using a linear combination of prototypical vectors to extend the representational capacity of the prototypes by generalization through interpolation and extrapolation.
Abstract: This paper proposes a novel pattern classification approach, called the nearest linear combination (NLC) approach, for eigenface based face recognition. Assume that multiple prototypical vectors are available per class, each vector being a point in an eigenface space. A linear combination of prototypical vectors belonging to a face class is used to define a measure of distance from the query vector to the class, the measure being defined as the Euclidean distance from the query to the linear combination nearest to the query vector (hence NLC). This contrasts to the nearest neighbor (NN) classification where a query vector is compared with each prototypical vector individually. Using a linear combination of prototypical vectors, instead of each of them individually, extends the representational capacity of the prototypes by generalization through interpolation and extrapolation. Experiments show that it leads to better results than existing classification methods.
98 citations
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20 Aug 2009TL;DR: Experimental evidence is provided which show that Polynomial and Radial Basis Function SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification.
Abstract: Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.
97 citations
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20 Jun 2005TL;DR: This paper presents an effective method to automatically extract ROI of facial surface, which mainly depends on automatic detection of facial bilateral symmetry plane and localization of nose tip, and builds a reference plane through the nose tip for calculating the relative depth values.
Abstract: This paper addresses 3D face recognition from facial shape. Firstly, we present an effective method to automatically extract ROI of facial surface, which mainly depends on automatic detection of facial bilateral symmetry plane and localization of nose tip. Then we build a reference plane through the nose tip for calculating the relative depth values. Considering the non-rigid property of facial surface, the ROI is triangulated and parameterized into an isomorphic 2D planar circle, attempting to preserve the intrinsic geometric properties. At the same time the relative depth values are also mapped. Finally we perform eigenface on the mapped relative depth image. The entire scheme is insensitive to pose variance. The experiment using FRGC database v1.0 obtains the rank-1 identification score of 95%, which outperforms the result of the PCA base-line method by 4%, which demonstrates the effectiveness of our algorithm.
97 citations
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TL;DR: The maximum a posteriori (MAP) adaptation is introduced to the problem of SIS estimation, and it is demonstrated that SIS varies significantly from person to person, and most SISs are not similar to AIS.
96 citations