Margin-maximization discriminant analysis for face recognition

LDA (linear discriminant analysis) and its variants are popular for image-based classification problems such as face recognition. However, their performance is inherently unstable when the samples are sparse. We propose a new type of discriminant analysis called MMDA (margin-maximization discriminant analysis), which derives features by maximizing the average margin between the classes. The method does not require S_W (within-class scatter matrix) to be non-singular and well-conditioned as it does not involve its inverse term, and the features can be directly derived from the input space. A computational trick has also been proposed for MMDA to handle high-dimensional data. We conducted intensive tests on ORL and UMIST face databases, and the results show that MMDA is a good replacement of LDA for the sparse sample problem.