Subspace LDA Methods for Solving the Small Sample Size Problem in Face Recognition
نویسندگان
چکیده
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 the LDA-based method is applied for classification. This paper investigates 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. Experimental results tested on three publically available face databases: JAFFE, ORL, and FEI, show that LDA without reducing image size by PCA projection is the worst and BDPCA plus LDA performs better than the other methods for a huge size of database.
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