Biometric identification and verification

• US 10,002,287 B2
• Filed: 06/15/2017
• Issued: 06/19/2018
• Est. Priority Date: 04/25/2008
• Status: Expired due to Fees

• Current Assignee: Aware Incorporated (Crowley Holdings Preferred LLC)
• Original Assignee: Aware Incorporated (Crowley Holdings Preferred LLC)
• Inventors: Benini, David
• Primary Examiner(s): Kholdebarin, Iman K
• Application Number:
  US15/623,709
• Publication Number:
  US 20170286757A1
• Time in Patent Office: 369 Days
• Field of Search: None
• 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 of using impostor match behavior of biometric gallery samples to add a false match probability score to a match score to assist with match/no match decisions to identify an individual based on a biometric sample comprising:

determining, using an interconnected processor, memory, one or more databases and one or more modules;

a first match score between a biometric probe sample and a gallery sample;

as the false match probability score, a plurality of gallery match scores between the gallery sample and a plurality of other samples;

a number of the plurality of gallery match scores that are greater than the determined first match score;

a plurality of probe match scores between the probe sample and the plurality of other samples;

a number of the plurality of probe match scores that are greater than the determined first match score; and

automatically performing a process, using the interconnected processor and memory, to attempt to identify the individual by using information from the above determining steps to improve match/no match decisions for one or more of;

biometric enabled access control, fingerprint search, facial recognition, retinal identification and iris identification.

7. The method of claim 1, wherein the method is for a one-to-many search.

8. The method of claim 1, wherein a single probe sample P1 is compared against a gallery G1 of samples G(x).

9. The method of claim 1, further comprising indicating, when compared to probe sample P1, that three gallery samples G(a), G(b) and G(c) yield match scores equal to or greater than an operating match threshold.

10. The method of claim 9, wherein for each of three probe/gallery sample pairs P1:

G(a), P1;

G(b), and P1;

G(c), the following is counted;

a) within a corpus of false match score data yielded from the comparison between the probe sample P1 and all gallery G(x) samples, an observed number of occurrences of the false match score being greater than the match score result between the probe P1 and gallery samples G(a), G(b), and G(c);

b) within a corpus of false match score data yielded from the comparison between gallery samples G(a), G(b), and G(c) and the rest of the gallery G1, an observed number of occurrences of false match scores above the match score result of the pair; and

c) a probability of a false match resulting from either P1 or G(x).

2. A method of using impostor match behavior of biometric gallery samples to add a false match probability score to a match score to assist with match/no match decisions to identify an individual when there is a multi-sample probe comprising:

determining, using an interconnected processor, memory, one or more databases and one or more modules;

a first match score between a first probe sample and a first gallery sample;

as the false match probability score, a plurality of gallery match scores between the first gallery sample and a plurality of other samples;

a number of the plurality of gallery match scores that are greater than the determined first match score;

a plurality of probe match scores between the first probe sample and the plurality of other samples;

a number of the plurality of probe match scores that are greater than the determined first match score;

a second match score of a second probe sample and a second gallery sample;

a plurality of gallery match scores between the second gallery sample and the plurality of other samples;

a number of the plurality of gallery match scores that are greater than the determined second match score;

a plurality of probe match scores between the second probe sample and the plurality of other samples;

a number of the plurality of probe match scores that are greater than the determined second match score;

combining, using the processor and the memory, information from the first probe sample and the second probe sample to determine a false match probability level; and

automatically performing a process, using the interconnected processor and memory, to attempt to identify an individual by using information from the above steps to improve the match/no match decisions for one or more of biometric-enabled access control, fingerprint search, facial recognition, retinal identification and iris identification.

11. The method of claim 2, wherein the first probe sample is a right sample.

12. The method of claim 2, wherein the second probe sample is a left sample.

3. A method of using impostor match behavior of biometric gallery samples to add a false match probability score to a match score to assist with match/no match decisions to identify an individual in a multi-modal environment comprising:

determining, using an interconnected processor, memory, one or more databases and one or more modules;

a first match score of a first probe sample corresponding to a mode and a first gallery sample;

as the false match probability score, a plurality of gallery match scores between the first gallery sample and a plurality of other samples;

a number of the plurality of gallery match scores that are greater than the determined first match score;

a plurality of probe match scores between the first probe sample and a plurality of other samples;

a number of the plurality of probe match scores that are greater than the determined first match score;

a second match score of a second probe sample corresponding to a second mode and a second gallery sample;

a plurality of gallery match scores between the second gallery sample and a plurality of other samples;

a number of the plurality of gallery match scores that are greater than the determined second match score;

a plurality of probe match scores between the second probe sample and a plurality of other samples;

a number of the plurality of probe match scores that are greater than the determined second match score;

combining, using the processor and the memory information from the first probe sample and the second probe sample to determine a false match probability level; and

automatically performing a process, using the interconnected processor and memory, to attempt to identify an individual by using information from the above steps to improve the match/no match decisions for one or more of biometric-enabled access control, fingerprint search, facial recognition, retinal identification and iris identification.

13. The method of claim 3, wherein a first mode is a fingerprint.

14. The method of claim 3, wherein a second mode is a face.

4. A system that uses impostor match behavior of biometric gallery samples to add a false match probability score to a match score to assist with match/no match decisions to identify an individual comprising:

a match score module, a processor and memory in communication with one another and configured to;

determine a first match score between a biometric probe sample from an access control system or a fingerprint scanner and a gallery sample;

determine, as the false match probability score, a plurality of gallery match scores between the gallery sample and a plurality of other samples;

determine a number of the plurality of gallery match scores that are greater than the determined first match score;

determine a plurality of probe match scores between the probe sample and the plurality of other samples;

determine a number of the plurality of probe match scores that are greater than the determined first match score; and

automatically performing a process, using the processor and memory, to attempt to identify an individual by using information from the above determining steps to improve the match/no match decisions for one or more of biometric-enabled access control, fingerprint search, facial recognition, retinal identification and iris identification.

15. The system of claim 4, wherein the system is for a one-to-many search.

16. The system of claim 4, wherein a single probe sample P1 is compared against a gallery G1 of samples G(x).

17. The system of claim 4, further comprising an alert module that indicates, when compared to probe sample P1, that three gallery samples G(a), G(b), and G(c) yield match scores equal to or greater than an operating match threshold.

18. The system of claim 17, wherein for each of three probe/gallery sample pairs P1:

G(a), P1;

G(b), and P1;

G(c), the following is counted;

a) within a corpus of false match score data yielded from the comparison between the probe sample P1 and all gallery G(x) samples, an observed number of occurrences of the false match score being greater than the match score result between the probe P1 and gallery samples G(a), G(b), and G(c);

b) within a corpus of false match score data yielded from the comparison between gallery samples G(a), G(b), and G(c) and the rest of the gallery G1, an observed number of occurrences of false match scores above the match score result of the pair; and

c) a probability of a false match resulting from either P1 or G(x).

5. A system that uses impostor match behavior of biometric gallery samples to add a false match probability score to a match score to assist with match/no match decisions to identify an individual when there is a multi-sample probe comprising:

a match score module, a processor and memory in communication with one another and configured to;

determine a first match score between a first probe sample from an access control system or a fingerprint scanner and a first gallery sample;

determine, as the false match probability score, a plurality of gallery match scores between the first gallery sample and a plurality of other samples;

determine a number of the plurality of gallery match scores that are greater than the determined first match score;

determine a plurality of probe match scores between the first probe sample and the plurality of other samples;

determine a number of the plurality of probe match scores that are greater than the determined first match score;

determine a second match score of a second probe sample and a second gallery sample;

determine a plurality of gallery match scores between the second gallery sample and the plurality of other samples;

determine a number of the plurality of gallery match scores that are greater than the determined second match score;

determine a plurality of probe match scores between the second probe sample and the plurality of other samples;

determine a number of the plurality of probe match scores that are greater than the determined second match score;

combine, by the processor, information from the first probe sample and the second probe sample to determine a false match probability level; and

automatically perform a process, by the processor and memory, to attempt to identify an individual by using information from the above steps to improve the match/no match decisions for one or more of biometric-enabled access control, fingerprint search, facial recognition retinal identification and iris identification.

19. The system of claim 5, wherein the first probe sample is a right sample.

20. The system of claim 5, wherein the second probe sample is a left sample.

6. A system that uses impostor match behavior of biometric gallery samples to add a false match probability score to a match score to assist with match/no match decisions in a multi-modal environment comprising:

a match score module, a processor and memory in communication with one another and configured to;

determine a first match score of a first probe sample from an access control system or a fingerprint scanner corresponding to a mode and a first gallery sample;

determine, as the false match probability score, a plurality of gallery match scores between the first gallery sample and a plurality of other samples;

determine a number of the plurality of gallery match scores that are greater than the determined first match score;

determine a plurality of probe match scores between the first probe sample and a plurality of other samples;

determine a number of the plurality of probe match scores that are greater than the determined first match score;

determine a second match score of a second probe sample corresponding to a second mode and a second gallery sample;

determine a plurality of gallery match scores between the second gallery sample and a plurality of other samples;

determine a number of the plurality of gallery match scores that are greater than the determined second match score;

determine a plurality of probe match scores between the second probe sample and a plurality of other samples;

determine a number of the plurality of probe match scores that are greater than the determined second match score; and

combine, using the processor, information from the first probe sample and the second probe sample to determine a false match probability level; and

automatically performing a process, by the processor and memory, to attempt to identify an individual by using information from the above steps to improve the match/no match decisions for one or more of biometric-enabled access control, fingerprint search, facial recognition retinal identification and iris identification.

21. The system of claim 6, wherein a first mode is a fingerprint.

22. The system of claim 6, wherein a second mode is a face.