Face recognition using parzenfaces

Authors:
Zhirong Yang;Jorma Laaksonen
Affiliations:
Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo, Finland;Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo, Finland
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 9
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Topics in matrix analysis

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Abstract

A novel discriminant analysis method is presented for the face recognition problem. It has been recently shown that the predictive objectives based on Parzen estimation are advantageous for learning discriminative projections if the class distributions are complicated in the projected space. However, the existing algorithms based on Parzen estimators require expensive computation to obtain the gradient for optimization. We propose here an accelerating technique by reformulating the gradient and implement its computation by matrix products. Furthermore, we point out that regularization is necessary for high-dimensional face recognition problems. The discriminative objective is therefore extended by a smoothness constraint of facial images. Our Parzen Discriminant Analysis method can be trained much faster and achieve higher recognition accuracies than the compared algorithms in experiments on two popularly used face databases.