System and method for implementing push technology in a wireless financial transactionDate:2021-09-10
Systems Training for Emotional Predictability and Problem Solving (STEPPS) group treatment for offenders with borderline personality disorder.
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 interest in face recognition is moving toward real-world applications and uncontrolled sensing environments. An important application of interest is automated surveillance, where the objective is to recognize and track people who are on a watchlist. For this open world application, a large number of cameras that are increasingly being installed at many locations in shopping malls, metro systems, airports, etc., will be utilized. While a very large number of people will approach or pass by these surveillance cameras, only a small set of individuals must be recognized. That is, the system must reject every subject unless the subject happens to be on the watchlist. While humans routinely reject previously unseen faces as strangers, rejection of previously unseen faces has remained a difficult aspect of automated face recognition. In this paper, we propose an approach motivated by human perceptual ability of face recognition which can handle previously unseen faces. Our approach is based on identifying the decision region(s) in the face space which belong to the target person(s). This is done by generating two large sets of borderline images, projecting just inside and outside of the decision region. For each person on the watchlist, a dedicated classifier is trained. Results of extensive experiments support the effectiveness of our approach. In addition to extensive experiments using our algorithm and prerecorded images, we have conducted considerable live system experiments with people in realistic environments.
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