PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Face recognition using Parzenfaces
Zhirong Yang and Jorma Laaksonen
In: International Conference on Artificial Neural Networks (ICANN’07), 9-13 Sep 2007, Porto, Portugal.

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.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:3650
Deposited By:Jorma Laaksonen
Deposited On:14 February 2008