A course of H∞0Econtrol theory
A course of H∞0Econtrol theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Null space versus orthogonal linear discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
An Innovative Weighted 2DLDA Approach for Face Recognition
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
An Innovative Weighted 2DLDA Approach for Face Recognition
Journal of Signal Processing Systems
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In this paper, we aim to develop a new classifier for increasing the worst case performance for individual person. Technically, we adopt the idea from LDA and improve the worst recognition performance for individuals. This is achieved via introducing different weighting coefficients in LDA optimization process for obtaining the projection matrix. By increasing the weighting coefficients associated with the smallest between-class distance, the pair of classes with the nearest distance can exert more powerful influence on the optimization process in derivation of the projection matrix. The algorithm is tested on the Extended YaleB dataset and the ORL dataset.