A critical issue of applying Linear Discriminant Analysis (LDA) is both the
singularity and instability of the within-class scatter matrix. In practice, particularly in
image recognition applications such as face recognition, there are often a large number of
pixels or pre-processed features available, but the total number of training patterns is
limited and commonly less than the dimension of the feature space. In this paper, a new
LDA-based method is proposed. It is based on a straighforward stabilisation approach for
the within-class scatter matrix. In order to evaluate its effectiveness, experiments on face
recognition using the well-known ORL and FERET face databases were carried out and
compared with other LDA-based methods. The results indicate that our method improves
the LDA classification performance when the within-class scatter matrix is not
only singular but also poorly estimated, with or without a Principal Component Analysis
intermediate step and using less linear discriminant features.