Banks Are Expected to Use More Behavioral Biometrics TechnologyDate:2021-09-11
A Fingerprint Matching Model using Unsupervised Learning Approach
A capacitive fingerprint system is the most widely used biometric identification method for smartphones. In this paper, we propose a RF sensor-based liveness detection scheme. This method solves the problem of spoofing attacks, which is a primary disadvantage to capacitive fingerprint sensors. The proposed scheme measures the inherent impedance characteristic difference of the target fingerprint caused by the eddy-current effect with an auto-balancing bridge method. The magnetic field is generated by a small form-factor inductor coil of $\phi =1.5$ mm. This detection scheme can be easily integrated with an existing capacitive fingerprint sensor by using the same CMOS process. The measured results demonstrate the liveness detection capability of the Si-graphite (silicone-graphite) and polyvinyl fake fingerprints that cannot be distinguished by conventional capacitive fingerprint sensors.
The system of the present invention comprises a personal Virtual Safety Deposit Box where users are able to enroll their identification methods, financial accounts and personal information. Once authenticated, this information is transferred to a master file within a central databank. Enrollment enables the user to link each item (collectively referred to as the "stored data") to any one of the plurality of identification methods they enter. Thereafter, the user may employ their enrolled identification methods to select a desired one of the stored data fields. A secure intermediary uses the identification method and a selection method to determine which of the stored data fields the user desires to employ by accessing a database containing each of the stored data fields and the corresponding selection method. The selected field is activated and any relevant outside agencies are notified of the transaction and the transaction is concluded.
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