We shall introduce the LDA approach in Section 4.1. Then , we shall explain how LDA is used for classification in our coarse to fine mechanism in Section 4.2. In this section, we shall discuss how LDA can be applied to form coarse to fine clustering at different resolutions. Assume that the current resolution is i. Using FST, the original features are projected into a lower dimensional algebraic feature space, which returns optimal discriminability for the classes.
G are the new features, and a ij are the coefficients.
We further modified the FST to account for the fact that some classes have a significantly greater variance in feature space than others. A Euclidean distance classifier is used to perform classification in the mapped space for these two face recognition systems. This was one of their best training sets found after extensive experimentation. Figure 2 shows a series of experimental results. The remaining images were the retrieved database images that had the 11 closest Euclidean distances to the query image.