System and Method for Detecting Potential Fraud Between a Probe Biometric and a Dataset of Biometrics
First Claim
1. A method for detecting potential fraud between a probe and a plurality of entries in a dataset, wherein each entry in the dataset comprises an entry identifier and a plurality of gallery images, the method comprising:
- receiving the probe, the probe comprising a probe identifier and a plurality of probe images;
for each respective entry in the dataset;
spectrally clustering the plurality of probe images and the plurality of gallery images of the respective entry to determine whether the plurality of probe images and the plurality of gallery images collectively correspond to one or two clusters,when the plurality of probe images and the plurality of gallery images collectively correspond to two clusters;
determining whether the plurality of probe images exclusively belong to a first cluster and the plurality of gallery images exclusively belong to a second cluster, andif not, flagging a potential instance of fraud in the form of stolen identity between the probe and the respective entry;
when the plurality of probe images and the plurality of gallery images collectively correspond to one cluster;
if so, flagging a potential instance of fraud in the form of multiple identities for the probe and the respective entry.
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Abstract
A system and method for detecting a potential match between a candidate facial image and a dataset of facial images is described. Some implementations of the invention determine whether a candidate facial image (or multiple facial images) of a person taken, for example, at point of entry corresponds to one or more facial images stored in a dataset of persons of interest (e.g., suspects, criminals, terrorists, employees, VIPs, “whales,” etc.). Some implementations of the invention detect potential fraud in a dataset of facial images. In a first form of potential fraud, a same facial image is associated with multiple identities. In a second form of potential fraud, different facial images are associated with a single identity, as in the case, for example, of identity theft. According to various implementations of the invention, spectral clustering techniques are used to determine a likelihood that pairs of facial images (or pairs of facial image sets) correspond to the person or different persons.
12 Citations
26 Claims
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1. A method for detecting potential fraud between a probe and a plurality of entries in a dataset, wherein each entry in the dataset comprises an entry identifier and a plurality of gallery images, the method comprising:
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receiving the probe, the probe comprising a probe identifier and a plurality of probe images; for each respective entry in the dataset; spectrally clustering the plurality of probe images and the plurality of gallery images of the respective entry to determine whether the plurality of probe images and the plurality of gallery images collectively correspond to one or two clusters, when the plurality of probe images and the plurality of gallery images collectively correspond to two clusters; determining whether the plurality of probe images exclusively belong to a first cluster and the plurality of gallery images exclusively belong to a second cluster, and if not, flagging a potential instance of fraud in the form of stolen identity between the probe and the respective entry; when the plurality of probe images and the plurality of gallery images collectively correspond to one cluster; if so, flagging a potential instance of fraud in the form of multiple identities for the probe and the respective entry. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method for detecting potential fraud between a probe and a plurality of entries in a dataset, wherein each entry in the dataset comprises an entry identifier and a plurality of gallery biometrics, the method comprising:
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receiving the probe, the probe comprising a probe identifier and a plurality of probe biometrics; for each respective entry in the dataset; spectrally clustering the plurality of probe biometrics and the plurality of gallery biometrics of the respective entry to determine whether the plurality of probe biometrics and the plurality of gallery biometrics collectively correspond to one or two clusters, when the plurality of probe biometrics and the plurality of gallery biometrics collectively correspond to two clusters; determining whether the plurality of probe biometrics exclusively belong to a first cluster and the plurality of gallery biometrics exclusively belong to a second cluster, and if not, flagging a potential instance of fraud in the form of stolen identity between the probe and the respective entry; when the plurality of probe biometrics and the plurality of gallery biometrics collectively correspond to one cluster; if so, flagging a potential instance of fraud in the form of multiple identities for the probe and the respective entry. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22)
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23. A method for detecting potential fraud between a probe and a plurality of entries in a dataset, wherein each entry in the dataset comprises an entry identifier and a plurality of gallery images, the method comprising:
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receiving the probe, the probe comprising a probe identifier and a plurality of probe images; for each respective entry in the dataset; spectrally clustering the plurality of probe images and the plurality of gallery images of the respective entry to determine whether the plurality of probe images and the plurality of gallery images collectively correspond to one or two clusters, when the plurality of probe images and the plurality of gallery images collectively correspond to two clusters, determining whether a cluster vector corresponds to a predefined fraud case. - View Dependent Claims (24, 25, 26)
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Specification