HUMAN EYE DETECTION IN WAVELET DOMAIN USING DISCRIMINANT ANALYSIS

Abstract

In this paper, we have to shows overcomes the problems on the fisher linear discriminant in such as performance in two class problems.Using some techniques on the clustering method to implement the clustering –based discriminant(CDA) models to obtain the problem on this system. Parametric and non – parametric both clusters are handled in this system from face databases are handled feasibly widely on the CDA models. Using efficient classification technique such as KNN classifier to detect the person to matched input eye image. The observations can be projected onto the subspace, resulting in a set of variables that captures most of the clustering information available. The use of generalized hyperbolic mixtures gives a robust framework capable of dealing with skewed clusters. Although dimension reductionis increasingly in demand across many application areas, the authors are most familiarwith biological applications and so two of the three real data examples are within thatsphere. Simulated data are also used for illustration.