In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on
his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right)
features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features
of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion
method which employs both the canonical correlation process and the proposed serial concatenation. A deep belief network was
used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of
experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been
reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition
accuracy up to 99%. |
