Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Incremental principal component analysis for image processing.

Abstract: A simple method for updating the eigenvectors and eigenvalues of a covariance matrix when a new input sample is added is presented. This proposed method will be a solution for both rank-one modification problems of a symmetric matrix and adaptive principal component analysis.

3D face verification using a free-parts approach

Abstract: This paper proposes a novel approach to 3D face verification which divides the 3D face into separate parts. This method, termed 3D free-parts, considers each part of the face independently and consequently the spatial relationship is discarded for the purpose of obtaining many observations from each face. Experiments illustrate the validity of the face verification system where the distribution of features are modelled robustly using Gaussian Mixture Models. This approach demonstrates a significant improvement over the eigenfaces approach, lowering the false rejection rate from 9.83% to 4.48% at a false acceptance rate of 0.1%, in tests conducted on 3D face data from the face recognition grand challenge database.

Local Curvelet Based Classification Using Linear Discriminant Analysis for Face Recognition

Abstract: In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA, and independent component Analysis (ICA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet-LDA algorithm. Experimental results on ORL, YALE and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies. Keywords—Curvelet, Linear Discriminant Analysis (LDA) , Contourlet, Discreet Wavelet Transform, DWT, Block-based analysis, face recognition (FR).

Hierarchical linear combinations for face recognition

Abstract: A hierarchical representation consisting of two level linear combinations (LC) is proposed for face recognition. At the first level, a face image is represented as a linear combination (LC) of a set of basis vectors, i.e. eigenfaces. Thereby a face image corresponds to a feature vector (prototype) in the eigenface space. Normally several such prototypes are available for a face class, each representing the face under a particular condition such as in viewpoint, illumination, and so on. We propose to use the second level LC, that of the prototypes belonging to the same face class, to treat the prototypes coherently. The purpose is to improve face recognition under a new condition not captured by the prototypes by using a linear combination of them. A new distance measure called nearest LC (NLC) is proposed as opposed to the NN. Experiments show that our method yields significantly better results than the one level eigenface methods.

Evaluation of unsupervised feature extraction neural networks for face recognition

Abstract: In this paper, new appearances based on neural networks (NN) algorithms are presented for face recognition. Face recognition is subdivided into two main stages: feature extraction and classifier. The suggested NN algorithms are the unsupervised Sanger principal component neural network (Sanger PCNN) and the self-organizing feature map (SOFM), which will be applied for features extraction of the frontal view of a face image. It is of interest to compare the unsupervised network with the traditional Eigenfaces technique. This paper presents an experimental comparison of the statistical Eigenfaces method for feature extraction and the unsupervised neural networks in order to evaluate the classification accuracies as comparison criteria. The classifier is done by the multilayer perceptron (MLP) neural network. Overcoming of the problem of the finite number of training samples per person is discussed. Experimental results are implemented on the Olivetti Research Laboratory database that contains variability in expression, pose, and facial details. The results show that the proposed method SOFM/MLP neural network is more efficient and robust than the Sanger PCNN/MLP and the Eigenfaces/MLP, when used a few number of training samples per person. As a result, it would be more applicable to utilize the SOFM/MLP NN in order to accomplish a higher level of accuracy within a recognition system.