Matrix analysis
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
A New Approximation Method of the Quadratic Discriminant Function
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Journal of Cognitive Neuroscience
Using mixture covariance matrices to improve face and facial expression recognitions
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Toward patient identification using chest CT scan
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
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 biometric recognition applications, the number of training examples per class is limited and consequently the conventional quadratic classifier either performs poorly or cannot be calculated. Other non-conventional quadratic classifiers have been used in limited sample and high dimensional classification problems. In this paper, a new quadratic classifier called Maximum Entropy Covariance Selection (MECS) is presented. This classifier combines the sample group covariance matrices and the pooled covariance matrix under the principle of maximum entropy. This approach is a direct method that not only deals with the singularity and instability of the maximum likelihood covariance estimator, but also does not require an optimisation procedure. In order to evaluate the MECS effectiveness, experiments on face and fingerprint recognition were carried out and compared with other similar classifiers, including the Reguralized Discriminant Analysis (RDA), the Leave-One-Out Covariance estimator (LOOC) and the Simplified Quadratic Discriminant Function (SQDF). In both applications, using the publicly released databases FERET and NIST-4, the MECS classifier achieved the lowest classification error.