Local Discriminant Wavelet Packet Coordinates for Face Recognition
The Journal of Machine Learning Research
Recursive Bayesian Linear Discriminant for Classification
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Real time face and mouth recognition using radial basis function neural networks
Expert Systems with Applications: An International Journal
Improved Statistical Techniques for Multi-part Face Detection and Recognition
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Audio-video biometric recognition for non-collaborative access granting
Journal of Visual Languages and Computing
An incremental learning algorithm of recursive Fisher linear discriminant
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel multi-stage classifier for face recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A novel incremental linear discriminant analysis for multitask pattern recognition problems
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A modified support vector machine and its application to image segmentation
Image and Vision Computing
Fovea intensity comparison code for person identification and verification
Engineering Applications of Artificial Intelligence
A regularization for the projection twin support vector machine
Knowledge-Based Systems
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Fisher linear discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems as compared to traditional principal component analysis. However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage, a recursive procedure of calculating the discriminant features is suggested in this paper. The new algorithm incorporates the same fundamental idea behind FLD of seeking the projection that best separates the data corresponding to different classes, while in contrast to FLD the number of features that may be derived is independent of the number of the classes to be recognized. Extensive experiments of comparing the new algorithm with the traditional approaches have been carried out on face recognition problem with the Yale database, in which the resulting improvement of the performances by the new feature extraction scheme is significant