Multimodal Biometrics for Improving Automatic Teller Machine Security

A multimodal biometrics face and fingerprint recognition system is a computer application for automatically identifying or verifying a person from face by using cameras and fingerprint by using sensors or fingerprint readers or fingerprint scanners. Proposed paper uses Face and Fingerprint recognition technique for verification in ATM systems. There are two types. The first one is verification. Compare the two faces and fingerprint images and decide whether the user (current user image) is an genuine user or imposter. These are decision level. Second one is identification this is where the system compares the given input image to all other images in the database and gives a ranked list of matches. Multimodal biometrics verification system that verifies the presence of a user is genuine or not. Two modalities are currently used?face and fingerprint?but our theory can be readily extended to include more modalities. We show that verification imposes additional requirements on multimodal fusion when compared to conventional verification systems. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for continuous verification and propose new metrics against which we benchmark our system.