Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Face recognition by inverse fisher discriminant features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A novel one-parameter regularized kernel fisher discriminant method for face recognition
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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This paper addresses the small-size problem in Fisher Discriminant Analysis. We propose to use wavelet transform for preliminary dimensionality reduction and use a two-parameter regularization scheme for the within-class scatter matrix. The novelty of the proposed method comes from: (1) Wavelet transform with linear computation complexity is used to carry out the preliminary dimensionality reduction instead of employing a principal component analysis. The wavelet filtering also acts as smoothing out noise. (2) An optimal solution is found in the full space instead of a sub-optimal solution in a restricted subspace. (3) Detailed analysis for the contribution of the eigenvectors of the within-class scatter matrix to the overall classification performance is carried out. (4) An enhanced algorithm is developed and applied to face recognition. The recognition accuracy (rank 1) for the Olivetti database using only three images of each person as training set is 96.7859%. The experimental results show that the proposed algorithm could further improve the recognition performance.