Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced Fisherfaces for Robust Face Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved-LDA based face recognition using both facial global and local information
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Gabor wavelets and General Discriminant Analysis for face identification and verification
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Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Regularization of LDA for face recognition: a post-processing approach
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
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Post-processing on LDA's discriminant vectors for facial feature extraction
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Discriminant analysis based on kernelized decision boundary for face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
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We introduce in this paper two Enhanced Fisher Linear Discriminant (FLD) Models (EFM) in order to improve the generalization ability of the standard FLD based classifiers such as Fisherfaces. Similar to Fisherfaces, both EFM models apply first Principal Component Analysis (PCA) for dimensionality reduction before proceeding with FLD type of analysis. EFM-1 implements the dimensionality reduction with the goal to balance between the need that the selected eigenvalues account for most of the spectral energy of the raw data and the requirement that the eigenvalues of the within-class scatter matrix in the reduced PCA subspace are not too small. EFM-2 implements the dimensionality reduction as Fisherfaces do. It proceeds with the whitening of the within-class scatter matrix in the reduced PCA subspace and then chooses a small set of features (corresponding to the eigenvectors of the within-class scatter matrix) so that the smaller trailing eigenvalues are not included in further computation of the between-class scatter matrix. Experimental data using a large set of faces -- 1, 107 images drawn from 369 subjects and including duplicates acquired at a later time under different illumination -- from the FERET database shows that the EFM models outperform the standard FLD based methods.