Iris recognition systems and methods of using a statistical model of an iris for authentication

• US 10,311,300 B2
• Filed: 05/17/2017
• Issued: 06/04/2019
• Est. Priority Date: 05/18/2016
• Status: Expired due to Fees

• Current Assignee: EyeLock LLC (VOXX International Corporation)
• Original Assignee: EyeLock LLC (VOXX International Corporation)
• Inventors: Teverovskiy, Mikhail
• Primary Examiner(s): Park, Edward
• Assistant Examiner(s): Bloom, Nathan J
• Application Number:
  US15/597,927
• Publication Number:
  US 20170337424A1
• Time in Patent Office: 748 Days
• Field of Search: None
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 2218/04 Denoising
• CPC Class Codes: G06F 2221/2117 User registration
• CPC Class Codes: G06T 2207/30041 Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 3/04 Context-preserving transformations, e.g. by using an importance map panospheric to cylindrical image transformations G06T3/12
• CPC Class Codes: G06V 40/193 Preprocessing; Feature extraction
• CPC Class Codes: G06V 40/197 Matching; Classification
• CPC Class Codes: G06V 40/50 Maintenance of biometric data or enrolment thereof

Abstract

The present disclosure describes systems and methods of using iris data for authentication. A biometric encoder may translate an image of the iris into a rectangular representation of the iris. The rectangular representation may include a plurality of rows corresponding to a plurality of annular portions of the iris. The biometric encoder may extract an intensity profile from at least one of the plurality of rows, the intensity profile modeled as a stochastic process. The biometric encoder may obtain a stationary stochastic component of the intensity profile by removing a non-stationary stochastic component from the intensity profile. The biometric encoder may remove at least a noise component from the stationary component using auto-regressive based modeling, to produce at least a non-linear background signal, and may combine the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

Claims

1. A method of using iris data for authentication, comprising:

translating, by a biometric encoder, an image of the iris acquired by a sensor into a rectangular representation of the iris, the rectangular representation comprising a plurality of rows corresponding to a plurality of circular circumferences within the iris;

extracting an intensity profile from at least one of the plurality of rows;

determining, by the biometric encoder, a non-stationary component of the intensity profile;

obtaining, by the biometric encoder, a stationary component of the intensity profile by removing the non-stationary component from the intensity profile, the stationary component modeled as a stochastic process;

removing, by the biometric encoder, at least a noise component from the stationary component using at least one of auto-regressive (AR), moving average (MA) or auto-regressive moving average (ARMA) based modeling of the noise component, to produce at least a non-linear background signal; and

combining the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

2. The method of claim 1, further comprising identifying one or more periodic waveforms in the stationary component.

3. The method of claim 2, wherein removing the at least a noise component from the stationary component further comprises removing the identified one or more periodic waveforms from the stationary stochastic component to produce the at least the non-linear background signal.

4. The method of claim 2, further comprising removing the identified one or more periodic waveforms from the stationary stochastic component to produce a background component, and determining a width of an autocorrelation function of the background component.

5. The method of claim 4, further comprising setting a filter size of a first filter according to the determined width, for filtering or processing periodic waveforms identified from another iris image.

6. The method of claim 1, further comprising determining that a combination of the non-stationary component and the at least the non-linear background signal would produce a biometric template with better iris recognition performance than a biometric template produced using another combination or using only one of the non-stationary component or the non-linear background signal, according to a comparison of corresponding values of biometric signal to noise ratio (BSNR).

7. The method of claim 2, further comprising storing a representation of the identified one or more periodic waveforms for authenticating the person.

8. The method of claim 1, further comprising comparing the biometric template with stored or acquired data to authenticate the person.

9. The method of claim 1, wherein the stationary stochastic component comprises a signal that fluctuates around zero intensity.

10. The method of claim 1, wherein the intensity profile is modeled as a one-dimensional stochastic process with the stationary and non-stationary stochastic components.

11. A system of using iris data for authentication, comprising:

a sensor configured to acquire an image of an iris of a person; and

a biometric encoder configured to;

translate the image of the iris into a rectangular representation of the iris, the rectangular representation comprising a plurality of rows corresponding to a plurality of circular circumferences within the iris;

extracting an intensity profile from at least one of the plurality of rows;

determine a non-stationary component of the intensity profile;

obtain a stationary component of the intensity profile by removing the non-stationary stochastic component from the intensity profile, the stationary component modeled as a stochastic process;

remove at least a noise component from the stationary component using at least one of auto-regressive (AR), moving average (MA) or auto-regressive moving average (ARMA) based modeling of the noise component, to produce at least a non-linear background signal; and

combine the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

12. The system of claim 11, wherein the biometric encoder is further configured to identify one or more periodic waveforms in the stationary component.

13. The system of claim 12, wherein the biometric encoder is further configured to remove the identified one or more periodic waveforms from the stationary stochastic component to produce the at least the non-linear background signal.

14. The system of claim 13, wherein the biometric encoder is further configured to remove the identified one or more periodic waveforms from the stationary stochastic component to produce a background component, and determine a width of an autocorrelation function of the background component.

15. The system of claim 14, wherein the biometric encoder is further configured to set a filter size of a first filter according to the determined width, for filtering or processing periodic waveforms identified from another iris image.

16. The system of claim 14, wherein the biometric encoder is further configured to determine a texture noise threshold using the background component.

17. The system of claim 12, wherein the biometric encoder is further configured to store a representation of the identified one or more periodic waveforms for authenticating the person.

18. The system of claim 11, further comprising a processor configured to compare the biometric template with stored or acquired data to authenticate the person.

19. The system of claim 11, wherein the stationary stochastic component comprises a signal that fluctuates around zero intensity.

20. The system of claim 11, wherein the intensity profile is modeled as a one-dimensional stochastic process with the stationary and non-stationary stochastic components.