Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new algorithm for generalized optimal discriminant vectors
Journal of Computer Science and Technology
A New Direct LDA (D-LDA) Algorithm for Feature Extraction in Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
On the Discriminant Vector Method of Feature Selection
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
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In this paper we propose a study on dimensionality reduction for client specific discriminant analysis with application to face verification A new algorithm of face verification based on client specific discriminant analysis is developed Two aspects of improvement are made in the new algorithm First, a dimensionality reduction based on the between-class scatter matrix is introduced which is more efficient than that based on the population scatter matrix The second improvement lies in the use of a new Fisher criterion function which is introduced in order to reduce the computational complexity of the client specific discriminant analysis problem The experimental results obtained on the internationally recognized facial database XM2VTS using the Lausanne protocol show the effectiveness of the proposed method.