BIOMETRIC IDENTIFICATION AND VERIFICATION

• US 20120087549A1
• Filed: 04/27/2009
• Published: 04/12/2012
• 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 8,559,681 B2
• Time in Patent Office: Days
• US Class Current: 382/115
• 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 the threshold 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.

2-4. -4. (canceled)

5. A method for setting a threshold in a biometric system using genuine match score data to automate derivation of 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 genuine biometric sample pairs;

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

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

6-19. -19. (canceled)

20. A method of using previous genuine and impostor biometric match score data to automate derivation of an individualized verification match threshold score comprising:

for each individual enrolled in a biometric system;

generating a database of match scores observed between all genuine and all impostor samples, and

creating a first cumulative histogram data table recording a number of the impostor match scores that are greater than that score divided by the total number of samples; and

for each individual enrolled in the biometric system;

generating a database of match scores observed between all the genuine samples, and

creating a second cumulative histogram data table recording for each possible match score less than that score divided by the total number of samples;

utilizing a match threshold that results in a false match rate and false non-match rate matching performance within a targeted range.

21-24. -24. (canceled)

25. A method of using previous genuine and impostor biometric match score data to automate derivation of an individualized verification match threshold score comprising:

for each individual enrolled in a biometric system;

generating a database of match scores observed between all genuine and all impostor samples,

creating a first cumulative histogram data table recording the number of these impostor match scores that are greater than that score divided by the total number of samples; and

for each individual enrolled in the biometric system;

generating a database of match scores observed between all genuine samples,

creating a second cumulative histogram data table recording for each possible match score less than that score divided by the total number of samples; and

utilizing a match threshold that results in false match rate and false non-match rate matching performance within a targeted range.

26-31. -31. (canceled)

32. A system 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:

a match score module that creates a database of match scores between all unique impostor biometric sample pairs;

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

a threshold module that select a desired false match rate and applies the threshold that will result in the desired false match rate.

33-35. -35. (canceled)

36. A system for setting a threshold in a biometric system using genuine match score data to automate derivation of a match threshold score that results in a desired false match rate and false non-match rate comprising:

a match score module that creates a database of match scores between all unique genuine biometric sample pairs;

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

a threshold module that selects a desired false non-match rate and applies the threshold that will result in the desired false non-match rate.

37-50. -50. (canceled)

51. A system of using previous genuine and impostor biometric match score data to automate derivation of an individualized verification match threshold score comprising:

for each individual enrolled in a biometric system;

a match score module that generates a database of match scores observed between all genuine and all impostor samples, and

a histogram module that creates a first cumulative histogram data table recording a number of the impostor match scores that are greater than that score divided by the total number of samples; and

for each individual enrolled in the biometric system;

the match score module generates a database of match scores observed between all the genuine samples, and

a histogram module creates a second cumulative histogram data table recording for each possible match score less than that score divided by the total number of samples;

a threshold module that utilizes a match threshold that results in a false match rate and false non-match rate matching performance within a targeted range.

52-55. -55. (canceled)

56. A system of using previous genuine and impostor biometric match score data to automate derivation of an individualized verification match threshold score comprising:

for each individual enrolled in a biometric system;

a match score module that generates a database of match scores observed between all genuine and all impostor samples,

a histogram module that creates a first cumulative histogram data table recording the number of these impostor match scores that are greater than that score divided by the total number of samples; and

for each individual enrolled in the biometric system;

the match score module generates a database of match scores observed between all genuine samples,

the match score module creates a second cumulative histogram data table recording for each possible match score less than that score divided by the total number of samples; and

a threshold module that utilizes a match threshold that results in false match rate and false non-match rate matching performance within a targeted range.

57-60. -60. (canceled)