  This paper presents a multimodal biometric system of fingerprint and ear
biometrics. Scale Invariant Feature Transform (SIFT) descriptor based feature
sets extracted from fingerprint and ear are fused. The fused set is encoded by
K-medoids partitioning approach with less number of feature points in the set.
K-medoids partition the whole dataset into clusters to minimize the error
between data points belonging to the clusters and its center. Reduced feature
set is used to match between two biometric sets. Matching scores are generated
using wolf-lamb user-dependent feature weighting scheme introduced by
Doddington. The technique is tested to exhibit its robust performance.
