Feature Level Fusion of Palmprint and Iris

In many real-life usages, single modal biometric systems repeatedly face significant restrictions due to noise in sensed data, spoof attacks, data quality, nonuniversality, and other factors. However, single traits alone may not be able to meet the increasing demand of high accuracy in today’s biometric system.Multibiometric systems is used to increase the performance that may not be possible using single biometrics. In this paper we propose a novel feature level fusion that combines the information to investigate whether the integration of palmprint and iris biometric can achieve performance that may not be possible using a single biometric technology. Proposed system extracts Gabor texture from the preprocessed palm print and iris images. The feature vectors attained from different methods are in different sizes and the features from equivalent image may be correlated. Therefore, we proposed wavelet-based fusion techniques. Finally the feature vector is matched with stored template using KNN classifier. The proposed approach is authenticated for their accuracy on PolyU palmprint database fused with IITK iris database of 125 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 99.2% and with false rejection rate (FRR) of = 1.6%.