| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 102 === In face recognition, LDA often encounters the so-called “small sample size” (SSS) problem, also known as “curse of dimensionality”. This problem occurs when the dimensionality of the data is quite large in comparison to the number of available training images. One of the approaches for handling this situation is the subspace LDA. It is a two-stage framework: it first uses PCA-based method for dimensionality reduction, and then LDA-based method is applied for classification. In this thesis, we investigate four popular subspace LDA methods: “Fisherface”, “complete PCA plus LDA”, “IDAface” and “BDPCA plus LDA” and compare their effectiveness when handling the SSS problem in face recognition. Extensive experiments have been performed on three publically available face databases: the JAFFE, ORL and FEI databases. Experimental results show that among the subspace LDA methods under investigation, the performance of the BDPCA plus LDA method is the best for solving the SSS problem in face recognition.
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