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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18 Nov 2008
TL;DR: An efficient method of face detection based on skin color segmentation and principal components analysis (PCA) is proposed by segmenting image using color model to filter candidate faces roughly and eye-analogue segments at a given scale are discovered.
Abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and principal components analysis(PCA) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, PCA method is used to extract the relevant information in human faces.
6 citations
01 Jan 2016
TL;DR: A new 2DPCA method for low numerical rank matrices and based on orthogonal triangular (QR) factorization is proposed in this paper, which displays more efficiency in terms of computational complexity and shows their outperformances over a bunch of 1D and 2D PCA methods.
Abstract: Standard principal component analysis (PCA) is frequently applied to a set of 1D vectors. For a set of 2D objects such as images, a 2DPCA approach that computes principal components of row-row and column-column covariance matrices would be more appropriate. A new 2DPCA method for low numerical rank matrices and based on orthogonal triangular (QR) factorization is proposed in this paper. The QR-based 2DPCA displays more efficiency in terms of computational complexity. We also propose and discuss a new updating schema for 2DPCA called 2DIPCA showcasing its numerical stability and speed. The proposed methods are applied to image compression and recognition and show their outperformances over a bunch of 1D and 2D PCA methods in both the batch and incremental modes. Experiments are performed on three benchmark face databases. Results reveal that the proposed methods achieve relatively substantial results in terms of recognition accuracy, compression rate and speed.
6 citations
01 Dec 2008
TL;DR: It is interesting to find from experiments that the performance of the ldquoAverage Facerd quo is not independent of the face recognition approaches, and although face averaging increases the recognition accuracy of eigenface method, it impairs theperformance of LBP method.
Abstract: This study focuses on a recent paper ldquo100% Accuracy in Automatic Face Recognitionrdquo published on Science, in which an ldquoAverage Facerdquo is proposed and claimed to be capable of dramatically improving performance of a face recognition system. To reveal its working mechanism, we perform the averaging process using pose-varied synthetic images generated from 3D face database and conduct a comparative study to observe its effectiveness on holistic and local face recognition approaches. Two representative methods, i.e. eigenface and local binary pattern (LBP) are employed to perform the experiments. It is interesting to find from our experiments that the performance of the ldquoAverage Facerdquo is not independent of the face recognition approaches. Although face averaging increases the recognition accuracy of eigenface method, it impairs the performance of LBP method.
6 citations
04 Dec 2013
TL;DR: A study is presented on face detection using Principal Component Analysis as a paradigm for generating compact representation for the human face with the fourth, sixth, and seventh eigenfaces being particularly critical for classification.
Abstract: A study is presented on face detection using Principal Component Analysis as a paradigm for generating compact representation for the human face. The study will focus on the contribution of individual eigenfaces in the face-space for classification in order to extract a minimum encoding for very low resolution images. The fourth, sixth, and seventh eigenfaces are identified as being particularly critical for classification, with the lowest order eigenface having a significant discriminatory contribution.
6 citations
09 Jun 2013
TL;DR: This paper proposes a robust PCA (RPCA) approach to solve the outlier problem, by modeling the cost function as a weighted regression problem, and results illustrated the effectiveness of this approach.
Abstract: In principal component analysis (PCA), l2/l2-norm is widely used to measure coding residual. In this case, it assume that the residual follows Gaussian/Laplacian distribution. However, it may fail to describe the coding errors in practice when there are outliers. Toward this end, this paper propose a robust PCA (RPCA) approach to solve the outlier problem, by modeling the cost function as a weighted regression problem. In face recognition progress, the observation samples and testing sample be projected on the principal space firstly. After that, in the new projection space, the face be classified based on the sparse representation. Simulation results illustrated the effectiveness of this approach.
6 citations