Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
SOM of SOMs: self-organizing map which maps a group of self-organizing maps
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Baseline evaluations on the CAS-PEAL-R1 face database
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
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Fisherface is a popular subspace algorithm used in face recognition, and is commonly believed superior to another technique, Eigenface, due to its attempt to maximize the separability of training classes. However, the obtained discriminating subspace of the training set may not easily extend to unseen classes (thus poor generalization), as in the case of enrollment of new subjects. In this paper, we reduce the performance variance and improve the generalization of Fisherface by automatically selecting some representative classes for training, using a recently proposed neural network architecture SOM2. The experiments on ORL face database validate the proposed method.