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

作者：Jiang Jiayan^*; Zhang Liming; Furukawa Tetsuo

来源：Neurocomputing, 2008, 71(16-18): 3239-3246.

DOI：10.1016/j.neucom.2008.04.042

• Face recognition
• Fisherface
• Density approximation
• Self-organizing map

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 SOM2 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.

• 出版日期2008-10
• 单位复旦大学