A Novel Multimodal Biometric System based on Iris and Face

With the wide application, the recognition accuracy of unimodal biometric systems has to contend with a variety of problems such as background noise, signal noise and distortion, and environment or device variations. This paper combines face and iris features for developing a multimode biometric system, which is able to diminish the above mentioned problems of unimodal biometrics as well as to improve the performance of authentication system. We adopt an efficient feature-level fusion scheme for iris feature and face feature in series, and normalize the original features of iris and face using zscore model to eliminate the unbalance of magnitude and the distribution between two different kinds of feature vectors. Fisher discriminant analysis is used to select the distinguishing character of the fused feature in order to further enhance the performance of the system. The proposed algorithm is tested using CASIA iris database and two face databases (ORL database and Yale database). Experimental results reveal the multimodal biometrics verification is much more reliable and precise than unimodal biometric system.