A LDA-based Face Recognizing Algorithm of Regularization Parameters
Feng Da-zheng · Computer and Modernization · 2010
It is well-known that the application of Line Discriminant Analysis(LDA) to high-dimensional face recognition often suffers from the so-called the problem of Small Sample Size(SSS) and close to class overlap.A new LDA method is proposed in this paper.The SSS problem is resolved by defining within-class scatter matrices for balancing bias and variance estimate of eigenvalues;weighting between-class scatter matrices for preventing edge class from being overlapped;introducing a regularized Fisher's criterion for increasing the stability of the null space B of within-class scatter matrices to well project the samples into the optimal space.Extensive experimental results show the new LDA algorithm can solve the above problems and outperforms traditional methods by controlling the parameters according to the degree of the SSS problem.