%0 Conference Proceedings
%T A maximum uncertainty LDA-based approach for limited sample size problems - with application to face recognition
%A Thomaz, Carlos Eduardo,
%A Gillies, Duncan Fyfe,
%@affiliation Centro Universitario da FEI, Sao Paulo, Brazil,
%@affiliation Imperial College, London, UK,
%E Rodrigues, Maria Andr?ia Formico,
%E Frery, Alejandro C?sar,
%B Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
%8 9-12 Oct. 2005
%I IEEE Computer Society
%J Los Alamitos
%K LDA, maximum uncertainty LDA, limited sample size, face recognition.
%X 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 maximum uncertainty LDA-based method is proposed. It is based on a straightforward 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.