Face recognition using a kernel fractional-step discriminant analysis algorithm
Pattern Recognition
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
Adaptive quasiconformal kernel discriminant analysis
Neurocomputing
Subspace KDA Algorithm for Non-linear Feature Extraction in Face Identification
Computational Intelligence and Security
A Criterion for Learning the Data-Dependent Kernel for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Complete discriminant evaluation and feature extraction in kernel space for face recognition
Machine Vision and Applications
Improved kernel fisher discriminant analysis for fault diagnosis
Expert Systems with Applications: An International Journal
A multiexpert collaborative biometric system for people identification
Journal of Visual Languages and Computing
Normal maps vs. visible images: Comparing classifiers and combining modalities
Journal of Visual Languages and Computing
A novel kernel-based maximum a posteriori classification method
Neural Networks
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Framelet kernels with applications to support vector regression and regularization networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Regularization of LDA for face recognition: a post-processing approach
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
A novel fisher criterion based St-subspace linear discriminant method for face recognition
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Face recognition by inverse fisher discriminant features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
A novel one-parameter regularized kernel fisher discriminant method for face recognition
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Support vector machines with weighted regularization
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper addresses two problems in linear discriminant analysis (LDA) of face recognition. The first one is the problem of recognition of human faces under pose and illumination variations. It is well known that the distribution of face images with different pose, illumination, and face expression is complex and nonlinear. The traditional linear methods, such as LDA, will not give a satisfactory performance. The second problem is the small sample size (S3) problem. This problem occurs when the number of training samples is smaller than the dimensionality of feature vector. In turn, the within-class scatter matrix will become singular. To overcome these limitations, this paper proposes a new kernel machine-based one-parameter regularized Fisher discriminant (K1PRFD) technique. K1PRFD is developed based on our previously developed one-parameter regularized discriminant analysis method and the well-known kernel approach. Therefore, K1PRFD consists of two parameters, namely the regularization parameter and kernel parameter. This paper further proposes a new method to determine the optimal kernel parameter in RBF kernel and regularized parameter in within-class scatter matrix simultaneously based on the conjugate gradient method. Three databases, namely FERET, Yale Group B, and CMU PIE, are selected for evaluation. The results are encouraging. Comparing with the existing LDA-based methods, the proposed method gives superior results.