An Orthogonal Feature Extraction Method Based on the Within-class Preserving for Small Sample Size Problem
Lin Chun Yu · Acta Automatica Sinica · 2010
Orthogonal feature extraction methods are widely employed to enhance the discriminatory information for the face recognition task,but often suffer the small sample size problem which arises if the number of samples is smaller than the dimensionality of samples.To solve this problem,an orthogonal feature extraction method based on the within-class preserving is proposed.The proposed method redefines the within-class and between-class scatter matrices according to the space information among samples belonging to the same class,and then gives the new objective function.This method may encounter the small size sample problem when it is applied to face recognition task,and so we firstly map the original space into a low dimensional subspace,then the singularity of the total-scatter matrix can be avoided in this low dimensional subspace.It is proved that the discriminant vectors derived in this low dimensional subspace are equal to the discriminant vectors derived in the original space.Experimental results on face database demonstrate the effectiveness of the proposed method.