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: A linear pattern classification algorithm, adaptive principal component analysis (APCA), which first applies PCA to construct a subspaces for image representation; then warps the subspace according to the within-class co-variance and between-class covariance of samples to improve class separability is presented.
Abstract: Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. This problem becomes more serious in applications when only one sample images per class is available. In this paper, we present a linear pattern classification algorithm, adaptive principal component analysis (APCA), which first applies PCA to construct a subspaces for image representation; then warps the subspace according to the within-class co-variance and between-class covariance of samples to improve class separability. This technique performed well under variations in lighting conditions. To produce insensitivity to expressions, we rotate the subspace before warping in order to enhance the representativeness of features. This method is evaluated on the Asian face image database. Experiments show that APCA outperforms PCA and other methods in terms of accuracy, robustness and generalization ability.
30 citations
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TL;DR: Experimental results show that the ridge regressive bilinear model significantly outperforms other existing methods such as the eigenface, quotient image, and the bilInear model in terms of the recognition rate under a variety of illuminations.
Abstract: The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.
30 citations
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01 Sep 2009TL;DR: A general hill-climbing attack algorithm based on Bayesian adaption is used to test the vulnerability of an Eigenface-based approach for face recognition against indirect attacks, which shows a very high efficiency.
Abstract: We use a general hill-climbing attack algorithm based on Bayesian adaption to test the vulnerability of an Eigenface-based approach for face recognition against indirect attacks The attacking technique uses the scores provided by the matcher to adapt a global distribution, computed from a development set of users, to the local specificities of the client being attacked The proposed attack is evaluated on an Eigenface-based verification system using the XM2VTS database The results show a very high efficiency of the hill-climbing algorithm, which successfully bypassed the system for over 85% of the attacked accounts
30 citations
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01 Sep 1997
29 citations
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07 Nov 2002TL;DR: A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed that employs linear support vector machines (SVM) as classifier for the recognition tasks and higher recognition rates were obtained which show that the algorithm is effective.
Abstract: Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed. By the use of the polynomial functions as a kernel function in KPCA, the high order relationships can be utilized and the nonlinear principal components can be obtained. After we obtain the nonlinear principal components, we use GAs to select the optimal feature set for classification. At the recognition stage, we employed linear support vector machines (SVM) as classifier for the recognition tasks. Two face databases were used to test our algorithm and higher recognition rates were obtained which show that our algorithm is effective.
29 citations