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
Nowadays, Iris recognition is a method of biometric verification of the person authentication process based on the human iris unique pattern, which is applied to control system for high level security. It is a popular system for recognizing humans and essential to understand it. The objective of this method is to assign a unique subject for each iris image for authentication of the person and provide an effective feature representation of the iris recognition with the image analysis. This paper proposed a new optimization and recognition process of iris features selection by using proposed Modified ADMM and Deep Learning Algorithm (MADLA). For improving the performance of the security with feature extraction, the proposed algorithm is designed and used to extract the strong features identification of iris of the person with less time, better accuracy, improving performance in access control and in security level. The evaluations of iris data are demonstrated the improvement of the recognition accuracy. In this proposed methodology, the recognition of the iris features has been improved and it incorporates into the iris recognition systems.
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