Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Covariance Matrix Estimation and Classification With Limited Training Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Learning the Face Space - Representation and Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Journal of Cognitive Neuroscience
Visual analysis of the use of mixture covariance matrices in face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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In several pattern recognition problems, particularly in image recognition ones, there are often a large number of features available, but the number of training samples for each pattern is significantly less than the dimension of the feature space. This statement im plies that the sample group covariance matrices of ten used in the Gaussian maximum probability classifier are singular. A common solution to this problem is to assume that all groups have equal covariance matrices and to use as their estimates the pooled covariance matrix calculated from the whole training set. This paper uses an alternative estimate for the sample group covariance matrices, here called the mixture covariance, given by an appropriate linear combination of the sample group and pooled covariance matrices. Experiments were carried out to evaluate the performance associated with this estimate in two recognition applications: face and facial expression. The average recognition rates obtained by using the mixture covariance matrices were higher than the usual estimates.