An Algorithm of Two-dimensional Uncorrelated Discriminant Vectors
Haibo Liu · 2009
All of the existed algorithms in face recognition,which can obtain uncorrelated discriminant vectors,are based on image vector model,so they encounter so called sample problem. These algorithms,which are solved using recursive methods,require much computation time. So a new algorithm is proposed in this paper,which is called Two-dimensional Uncorrelated Discriminant Vectors based on an image matrix model. The new algorithm solves small sample size problem through whitening transform of within-class scatter matrix,which makes the model of extended Two-dimensional Linear Discriminant Analysis have similar form of Two-dimensional Principal Component Analysis model. Thus two algorithms were combined effectively,uncorrelated discriminant vectors can be obtained non-recursively. The new method computes fast while maintaining numerical stability. The numerical experiments on facial databases of ORL and Yale show that the proposed method has not only reduced the computation complexity but also achieved higher recognition accuracy,providing new thought on how to obtain uncorrelated discriminant vectors.