# US 2017/0286757 A1 · Biometric identification and verification

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- Number: US 2017/0286757 A1
- Document ID: US20170286757A1
- Status: Published application
- Kind: A1
- Application number: 15/623,709
- Filed: 2017-06-15
- Published: 2017-10-05
- Earliest claimed: 2008-04-25
- Assignees: Aware, Inc.
- Applicants: Aware, Inc.
- Inventors: David BENINI
- Web page: https://patentgrep.com/patent/US20170286757A1

## CPC

- G06V 10/764: using classification, e.g. of video objects
- G06F 16/24578: {using ranking}
- G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F 21/32: using biometric data, e.g. fingerprints, iris scans or voiceprints
- G06V 40/10: Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V 40/1365: {Matching; Classification}
- G06V 40/172: {Classification, e.g. identification}
- G06V 40/40: Spoof detection, e.g. liveness detection
- G06V 40/70: Multimodal biometrics, e.g. combining information from different biometric modalities
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## 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

### Claim 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.

### Claim at position 2

-60. (canceled)