Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Class-Incremental Generalized Discriminant Analysis
Neural Computation
An efficient algorithm for generalized discriminant analysis using incomplete Cholesky decomposition
Pattern Recognition Letters
Symmetrical null space LDA for face and ear recognition
Neurocomputing
Complete discriminant evaluation and feature extraction in kernel space for face recognition
Machine Vision and Applications
Direct kernel neighborhood discriminant analysis for face recognition
Pattern Recognition Letters
Fuzzy linear and nonlinear discriminant analysis algorithms for face recognition
Intelligent Data Analysis
Multi-view ear recognition based on moving least square pose interpolation
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Face recognition based on local steerable feature and random subspace LDA
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Discriminant analysis based on kernelized decision boundary for face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Recent advances in subspace analysis for face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A novel null space-based kernel discriminant analysis for face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Furthermore, kernel technique is incorporated into our null space method. Firstly, all samples are mapped to the kernel space through an efficient kernel function, called Cosine kernel, which have been demonstrated to increase the discriminating capability of the original polynomial kernel function. Secondly, a truncated NLDA is employed. The novel approach only requires one eigenvalue analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed method.