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

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.