Fast Kernel Foley-Sammon Discriminant Analysis with Application to Face Recognition
Jingyu Yang · 2009
Kernel Foley-Sammon Discriminant Analysis(KFSDA) is extensively applied on the field of pattern recognition with the capacity of extracting nonlinear orthonormal features from original samples.But each eigenvector should be achieved by calculating the corresponding generalized eigenfunction with this algorithm,and it is very time-consuming.In order to overcome this problem,a fast algorithm of KFSDA named Fast Kernel Foley-Sammon Discriminant Analysis(FKFSDA) was proposed.Experimental results on the ORL face database demonstrate the proposed algorithm not only outperforms Kernel Linear Discriminant Analysis(KLDA) in recognition rates,but also outperforms conventional Foley-Sammon Discriminant Analysis in feature extraction speed.