Abstract A regularized locality preserving discriminant analysis (RLPDA) for face recognition is proposed. Affected by the small sample size (SSS) problem and noises, zero eigenvalues and small eigenvalues of locality preserving within-class scatter matrix are inadequate. It degrades the performance of discriminant locality preserving projections (DLPP). In this paper, eigenvalues of locality preserving within-class scatter matrix are regularized by a reciprocal spectrum model, and the subspaces are weighted according to the regularized eigenvalues. Specifically, the face subspace is kept, the noise subspace is weakened, and the zero subspace is enhanced. Through the analysis of the distribution of discriminant information in data space, it is found that RLPDA utilizes the whole discriminant information. Hence, RLPDA improves the recognition accuracies and avoids the SSS problem in principal. The experimental results on FERET and UMIST face databases illustrate the effectiveness of the proposed RLPDA algorithm.
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2012, Vol. 25 
