THE FRAMEWORK DESIGN FOR INCREASING SECURITY OF MULTI-MODAL BIOMETRIC AUTHENTICATION SYSTEM WITH DNN

Afshan Ashraf, Isha Vats

Abstract


This research paper defines a multi-model system for verification based-on the biometric fusion of retina, finger vein and finger print recognition. We have proposed feature extraction in retina recognition model by using SIFT and MINUTIA feature extract at work in different levels.Security is the main concept in ATM (Automated Teller Machines) today. Multi-modal Biometric are secure as compared to uni-modal biometric as even if single trait destroys the other is present. The application of multi-modal biometrics can be ATM. The proposed work, adds considering of three biometric traits of a user namely retina, fingerprint and finger veins by implemented software later, these are pre-processed and combined (Fused) together for score level fusion approach used. Retina is selected as a biometric trait as no binary retina feature match unless they are of the similar user also retina has a good retina vessels pattern making it’s good verifying approach for a single as compared to other biometric traits. Security is found in the system by multi-modal biometric fusion of retina with finger vein and finger print. Feature Extraction approach and cryptography is used in-order to achieve security. Classification approach is considered by the designed software are pre-processed using feature extracted by MINUTIA, SIFT algorithm. The feature key points or minutiae points are fused at score level using distance average and later matched.The experimental result evaluated using MATLAB 2013a, illustrates the importance enhancement in the performance of multi-modal biometric with RSA and DNN have GAR and FAR %.

Keywords


Biometric, Fusion, Score Level Fusion, Munities and Scale Invariant Feature Transformation, deep Neural network.

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References


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DOI: https://doi.org/10.26483/ijarcs.v8i8.4611

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