Kernel maximum scatter difference based feature extraction and its application to face recognition
Pattern Recognition Letters
Kernel Weighted Scatter-Difference-Based Discriminant Analysis for Face Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Complete discriminant evaluation and feature extraction in kernel space for face recognition
Machine Vision and Applications
Scatter Difference NAP for SVM Speaker Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Fuzzy linear and nonlinear discriminant analysis algorithms for face recognition
Intelligent Data Analysis
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ACC'09 Proceedings of the 2009 conference on American Control Conference
A survey of multilinear subspace learning for tensor data
Pattern Recognition
An improved hybrid approach to face recognition by fusing local and global discriminant features
International Journal of Biometrics
Feature Extraction Using a Complete Kernel Extension of Supervised Graph Embedding
Neural Processing Letters
Kernel based enhanced maximum margin criterion for feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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There are two fundamental problems with the Fisher linear discriminant analysis for face recognition. One is the singularity problem of the within-class scatter matrix due to small training sample size. The other is that it cannot efficiently describe complex nonlinear variations of face images because of its linear property. In this letter, a kernel scatter-difference-based discriminant analysis is proposed to overcome these two problems. We first use the nonlinear kernel trick to map the input data into an implicit feature space F. Then a scatter-difference-based discriminant rule is defined to analyze the data in F. The proposed method can not only produce nonlinear discriminant features but also avoid the singularity problem of the within-class scatter matrix. Extensive experiments show encouraging recognition performance of the new algorithm.