  The physiological and behavioral trait is employed to develop biometric
authentication systems. The proposed work deals with the authentication of iris
and signature based on minimum variance criteria. The iris patterns are
preprocessed based on area of the connected components. The segmented image
used for authentication consists of the region with large variations in the
gray level values. The image region is split into quadtree components. The
components with minimum variance are determined from the training samples. Hu
moments are applied on the components. The summation of moment values
corresponding to minimum variance components are provided as input vector to
k-means and fuzzy kmeans classifiers. The best performance was obtained for MMU
database consisting of 45 subjects. The number of subjects with zero False
Rejection Rate [FRR] was 44 and number of subjects with zero False Acceptance
Rate [FAR] was 45. This paper addresses the computational load reduction in
off-line signature verification based on minimal features using k-means, fuzzy
k-means, k-nn, fuzzy k-nn and novel average-max approaches. FRR of 8.13% and
FAR of 10% was achieved using k-nn classifier. The signature is a biometric,
where variations in a genuine case, is a natural expectation. In the genuine
signature, certain parts of signature vary from one instance to another. The
system aims to provide simple, fast and robust system using less number of
features when compared to state of art works.
