ISSN : 1796-2048
Volume : 3    Issue : 1    Date : May 2008

Multiresolution Feature Based Fractional Power Polynomial Kernel Fisher Discriminant Model for
Face Recognition
Dattatray V. Jadhav, Jayant V. Kulkarni, and Raghunath S. Holambe
Page(s): 47-53
Full Text:
PDF (374 KB)

This paper prese nts a technique for face recognition which uses wavelet transform to derive
desirable facial features. Three level decompositions are used to form the pyramidal
multiresolution features to cope with the variations due to illumination and facial expression
changes. The fractional power polynomial kernel maps the input data into an implicit feature space
with a nonlinear mapping. Being linear in the feature space, but nonlinear in the input space, kernel
is capable of deriving low dimensional features that incorporate higher order statistic. The Linear
Discriminant Analysis is applied to kernel mapped multiresolution featured data. The effectiveness
of this Wavelet Kernel Fisher Classifier algorithm is compared with the different existing popular
algorithms for face recognition using FERET, ORL Yale and YaleB databases. This algorithm
performs better than some of the existing popular algorithms.

Index Terms
multiresolution, face recognition, kernel, linear discriminant analysis