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
Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Complete discriminant evaluation and feature extraction in kernel space for face recognition
Machine Vision and Applications
Letter: An uncorrelated fisherface approach
Neurocomputing
Face recognition technique using symbolic PCA method
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Face recognition technique using symbolic linear discriminant analysis method
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, we present a new radial basis kernel function (RBF) in symbolic kernel Fisher discriminant analysis (symbolic KFD) to extract nonlinear interval type features for face recognition. The kernel-based methods form a powerful paradigm, they are not favorable to deal with the challenge of large datasets of faces. We propose to scale up training task based on the interval data concept. Our investigation aims at extending KFD to interval data using new RBF kernel function. We adapt symbolic KFD to extract interval type nonlinear discriminating features, which are robust enough to varying facial expression, viewpoint and illumination. In the classification phase, we employ the minimum distance classifier with the squared Euclidean distance measure. The new algorithm has been successfully tested using four databases, namely, the ORL face database, the Yale face database, the Yale face database B and the FERET face database. The experimental results show that the symbolic KFD with the new RBF kernel function yields improved performance.