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2005
IEEE

A Maximum Uncertainty LDA-Based Approach for Limited Sample Size Problems : With Application to Face Recognition

9 years 1 months ago
A Maximum Uncertainty LDA-Based Approach for Limited Sample Size Problems : With Application to Face Recognition
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 discri...
Carlos E. Thomaz, Duncan Fyfe Gillies
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where SIBGRAPI
Authors Carlos E. Thomaz, Duncan Fyfe Gillies
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