Topic
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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TL;DR: PCA is used for face recognition to improve the accuracy of recognition and show that the proposed method has a high recognition rate for the face images in the experiments.
Abstract: As a numerical analysis technique,the main application of PCA is to simplify data and reduce data dimension The introduction of the PCA algorithm to face recognition can extract the most important features of the face image and remove the redundancy and noise of data In this paper,PCA is used for face recognition to improve the accuracy of recognition Experiments in the ORL and YALE face database show that the proposed method has a high recognition rate for the face images in the experiments
22 citations
17 May 2004
TL;DR: The principal subspace is derived from the intro-personal kernel space by developing a probabilistic analysis for kernel principal components for face recognition by exploiting the role of illumination and facial expression variations in face recognition.
Abstract: Intra-personal space modeling proposed by Moghaddam et al. has been successfully applied in face recognition. In their work the regular principal subspaces are derived from the intra-personal spacce using a principal componen analysis and embedded in a probabilistic formulation. In this paper, we derive the principal subspace from the intro-personal kernel space by developing a probabilistic analysis for kernel principal components for face recognition. We test this algorithm on a subset of the FERET database with illumination and facial expression variations. The recognition performance demonstrates its advantage over other traditional subspace approaches.
22 citations
TL;DR: A factored covariance model is proposed for matrix data, and a method for classification using a likelihood ratio criterion is developed, which has previously been used for evaluating the strength of forensic evidence.
Abstract: A dimension reduction technique is proposed for matrix data, with applications to face recognition from images. In particular, we propose a factored covariance model for the data under study, estimate the parameters using maximum likelihood, and then carry out eigendecompositions of the estimated covariance matrix. We call the resulting method factored principal components analysis. We also develop a method for classification using a likelihood ratio criterion, which has previously been used for evaluating the strength of forensic evidence. The methodology is illustrated with applications in face recognition.
22 citations
01 Jan 2006
22 citations
TL;DR: A novel statistical generative model to describe a face is presented, and is applied to the face authentication task, proposing to encode relationships between salient facial features by using a static Bayesian Network.
22 citations