BIOMETRIC IDENTIFICATION AND VERIFICATION

• US 20170286757A1
• Filed: 06/15/2017
• Published: 10/05/2017
• Est. Priority Date: 04/25/2008
• Status: Active Grant

• Current Assignee: Aware Incorporated (Crowley Holdings Preferred LLC)
• Original Assignee: Aware Incorporated (Crowley Holdings Preferred LLC)
• Inventors: BENINI, David
• Granted Patent: US 10,002,287 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G06V 40/1365 Matching; Classification
• CPC Class Codes: G06V 40/172 Classification, e.g. identification
• CPC Class Codes: G06V 40/40 Spoof detection, e.g. liveness detection
• CPC Class Codes: G06V 40/70 Multimodal biometrics, e.g. combining information from different biometric modalities

Abstract

In real biometric systems, false match rates and false non-match rates of 0% do not exist. There is always some probability that a purported match is false, and that a genuine match is not identified. The performance of biometric systems is often expressed in part in terms of their false match rate and false non-match rate, with the equal error rate being when the two are equal. There is a tradeoff between the FMR and FNMR in biometric systems which can be adjusted by changing a matching threshold. This matching threshold can be automatically, dynamically and/or user adjusted so that a biometric system of interest can achieve a desired FMR and FNMR.

Claims

1. A method for setting a threshold in a biometric system using imposter match score data to derive a match threshold score that results in a desired false match rate and false non-match rate comprising:

creating a database of match scores between all unique impostor biometric sample pairs;

creating a cumulative histogram data table recording for each possible match score outcome a number of match scores observed greater than that value divided by the total number of samples; and

selecting a desired false match rate and applying the threshold that will result in the desired false match rate.

2-60. -60. (canceled)