Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient face verification method in a transformed domain
Pattern Recognition Letters
Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach
Neural Processing Letters
Bayes Optimality in Linear Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Score fusion in text-dependent speaker recognition systems
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
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This paper compares the recently developed biometric dispersion matcher (BDM) with the classical linear discriminant analysis (LDA) for biometric pattern recognition. BDM is extended to the BDM with simultaneously diagonalization (BDMSD) of the covariance matrices and, from a theoretical point of view, it is demonstrated that the feature selection of LDA and BDMSD are equivalent. However, LDA uses the between-class scatter matrix (S"B) only for feature selection and BDMSD also uses it for classification. This implies a set of advantages. Mainly the BDMSD offers better generalization capability for classifying samples of users that have not been used for training the classifier. Experimental results show that BDM and BDMSD outperform LDA in face recognition and hand-geometry recognition. These two cases correspond to very different situations: number of samples greater than their dimensionality (hand-geometry) and number of samples similar to their dimensionality (face recognition).