A new covariance estimate for Bayesian classifiers in biometric recognition

Authors:
C. E. Thomaz;D. F. Gillies;R. Q. Feitosa
Affiliations:
Dept. of Comput., Imperial Coll. London, UK;-;-
Venue:
IEEE Transactions on Circuits and Systems for Video Technology
Year:
2004

Citing 0
Cited 11

A multivariate statistical analysis of the developing human brain in preterm infants

Image and Vision Computing
Multivariate Statistical Differences of MRI Samples of the Human Brain

Journal of Mathematical Imaging and Vision
Perturbation LDA: Learning the difference between the class empirical mean and its expectation

Pattern Recognition
Letters: Feature extraction using fuzzy inverse FDA

Neurocomputing
Enhanced direct linear discriminant analysis for feature extraction on high dimensional data

AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Rapid and brief communication: Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion

Pattern Recognition
Robust discriminant analysis of latent semantic feature for text categorization

FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Face recognition using heteroscedastic weighted kernel discriminant analysis

ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
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
Regularized feature extractions and support vector machines for hyperspectral image data classification

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Feature extraction using a fast null space based linear discriminant analysis algorithm

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In many biometric pattern-recognition problems, the number of training examples per class is limited, and consequently the sample group covariance matrices often used in parametric and nonparametric Bayesian classifiers are poorly estimated or singular. Thus, a considerable amount of effort has been devoted to the design of other covariance estimators, for use in limited-sample and high-dimensional classification problems. In this paper, a new covariance estimate, called the maximum entropy covariance selection (MECS) method, is proposed. It is based on combining covariance matrices under the principle of maximum uncertainty. In order to evaluate the MECS effectiveness in biometric problems, experiments on face, facial expression, and fingerprint classification were carried out and compared with popular covariance estimates, including the regularized discriminant analysis and leave-one-out covariance for the parametric classifier, and the Van Ness and Toeplitz covariance estimates for the nonparametric classifier. The results show that, in image recognition applications whenever the sample group covariance matrices are poorly estimated or ill posed, the MECS method is faster and usually more accurate than the aforementioned approaches in both parametric and nonparametric Bayesian classifiers.