The null space N(St) of total scatter matrix St contains no useful information for pattern classification. So, discarding the null space N(St) results in dimensionality reduction without loss discriminant power. Combining this subspace technique with proposed rank lifting scheme, a new regularized Fisher discriminant (SL-RFD) method is developed to deal with the small sample size (S3) problem in face recognition. Two public available databases, namely FERET and CMU PIE databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed SL-RFD method gives the best performance. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Chen, W. S., Yuen, P. C., Huang, J., Lai, J., & Tang, J. (2005). A novel regularized fisher discriminant method for face recognition based on subspace and rank lifting scheme. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3784 LNCS, pp. 152–159). Springer Verlag. https://doi.org/10.1007/11573548_20
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