Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric discriminant analysis and nearest neighbor classification
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
This paper presents a new nonparametric linear feature extraction method coined geometrically intuitive marginal discriminant analysis (IMDA). Motivated by the law of cosines in trigonometry, we characterize the square local margin by a weighted difference of the square between-class distance and the square within-class distance. Based on this characterization, we design a class margin criterion which is used to determine an optimal transform matrix such that the class margin is maximized in the transformed space. The proposed method was applied to face recognition and evaluated on the Yale and the FERET databases. Experimental results demonstrate the effectiveness of the proposed method.