A class density approximation neural network for improving the generalization of Fisherface

Authors:
Jiayan Jiang;Liming Zhang;Tetsuo Furukawa
Affiliations:
Electronic Engineering Department, Fudan University, 220 Handan Road, Shanghai, China;Electronic Engineering Department, Fudan University, 220 Handan Road, Shanghai, China;Kyushu Institute of Technology, Kitakyushu 808-0196, Japan
Venue:
Neurocomputing
Year:
2008

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Abstract

Fisherface is a popular dimensionality reduction technique in face recognition. The obtained discriminative subspace, however, may not generalize well to unseen classes, as in the case of enrollment of new identities. In this paper, a class density approximation network based on SOM^2 is introduced to derive ''prototype classes'' for training, which improves the generalization of Fisherface and reduces its performance variance resulting from random selections of training classes. We also propose a splitting method to estimate the number of prototype classes needed in approximation. Experiments on synthesized data and real face databases validate the effectiveness of our method.