Shaping classification boundaries in template protection systems

• US 8,122,260 B2
• Filed: 05/23/2006
• Issued: 02/21/2012
• Est. Priority Date: 06/01/2005
• Status: Active Grant

• Current Assignee: Genkey Netherlands BV
• Original Assignee: PRIV ID BV
• Inventors: Van Der Veen, Minne, Kevenaar, Thomas Andreas Maria, Bruekers, Alphons Antonius Maria Lambertus, Akkermans, Antonius Hermanus Maria, Willems, Franciscus Maria Joannes
• Primary Examiner(s): HOFFMAN, BRANDON S
• Application Number:
  US11/916,094
• Publication Number:
  US 20080199041A1
• Time in Patent Office: 2,100 Days
• Field of Search: None
• US Class Current: 713/186
• CPC Class Codes: G06F 21/31 User authentication
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G07C 9/37 using biometric data, e.g. fingerprints, iris scans or voice recognition

Abstract

The invention relates to a method of authentication of a physical object and an apparatus applying said method. The method uses a helper data (W1) and a control value (V1) associated with a reference object to generate a first property set (C1) using the helper data (W1) and a metric (Y) associated with the physical object. It further comprises a step to generate a second property set (S1) using a noise compensating mapping (NCM) on the first property set (C1), as well as a step to establish a sufficient match between the physical object and the reference object using the second property set (S1) and the first control value (V1). The method is characterized by a step that generates an error measure (ERR) by quantifying the noise removed by the noise compensating mapping (NCM) using the first property set (C1) and information derived from the noise compensating mapping (NCM). Error measure (ERR) is subsequently used for generating an authentication decision (D). Also provided is an apparatus configured to carry out the method.

Claims

1. An authentication apparatus for authentication of a physical object comprising

an enrollment means (ENRL), and

an authentication means (AUTH), wherein

said enrollment means are arranged in

measuring reference characteristics of said physical object;

generating and storing template data based on said measured reference characteristics;

generating and storing a helper data (W1) based on said template data

generating and storing a control value (V1);

the authentication means being arranged in;

obtaining a metric (Y) from the physical object to be authenticated;

and further comprising;

a first generation means (GM1) arranged to generate a first property set (C1) using the helper data (W1) and the metric (Y) obtained from the physical object,

a second generation means (GM2) arranged to subject said first property data set (C1) to a noise compensating mapping (NCM) to compensate for measured noise in said metric (Y) thereby generating a second property set (S1),

a comparing means (CMP) arranged to establish a sufficient match between the physical object and the reference object, by verifying whether the second property set (S1) is within a classification boundary (REG1) of the reference object using the first control value (V1) and wherein a noise robust mapping (NRM) determines the classification boundary,

the authentication apparatus characterized in that it further comprises;

a third generation means (GM3) arranged to generate an error measure (ERR) by quantifying the noise removed by the noise compensating mapping (NCM) using the first property set (C1) and the second property set (S2), and

an authentication decision means (ADM) arranged to use said error measure (ERR) to generate an authentication decision (D).

2. An authentication apparatus as claimed in claim 1, where the third generation means (GM3) is arranged to generate an error measure (ERR) that quantifies the difference between the first property set (C1) and the second property set (S1).

3. An authentication apparatus as claimed in claim 1, where the third generation means (GM3) arranged to generate an error measure (ERR) comprises:

a fourth generation means (GM4) arranged to generate the third property set (C2) using the noise robust mapping (NRM) on information comprising the second property set (S1), and

a fifth generation means (GM5) arranged to generate the error measure (ERR) by quantifying the difference between the first property set (C1) and the third property set (C2).

4. An authentication apparatus as claimed in claim 1, where the authentication decision means (ADM) is arranged to use information comprising the error measure (ERR) and the first control value (V1) associated with both the reference object and the helper data (W1), to generate the authentication decision (D) identifying whether the physical object corresponds with the reference object.

5. An authentication apparatus as claimed in claim 1, where the authentication decision means (ADM) is arranged to use information comprising the error measure (ERR) and the first control value (V1) associated with both the reference object and the helper data (W1), to generate a probability measure (P) related to the authentication decision (D).

6. An authentication apparatus for identification of a physical object using both a helper data (W1) and a control value (V1) associated with a reference object, the apparatus according to claim 1, further comprising an identity establishing means arranged to establish the identity of the physical object as being identical to that of the reference object.

7. A computer controlled method suitable of authentication of a physical object in a system employing template data protection,

comprising an enrollment process consisting of the steps of:

measuring reference characteristics of the physical object;

generating template data based on said measured reference characteristics;

generating a helper data (W1) based on said template data

generating a control value (V1);

the method further including an authentication process consisting of the steps of

obtaining a metric (Y) from the physical object to be authenticated;

generating a first property data set (C1) based on the helper data (W1) and the metric (Y),

subjecting said first property data set (C1) to a noise compensating mapping (NCM) to compensate for measured noise in said metric (Y) thereby;

generating a second property data set (S1),

for authenticating purposes of the physical object verifying whether the second property data set (S1) is within a classification boundary (REG1) of the reference object using the first control value (V1) and wherein a noise robust mapping (NRM) determines the classification boundary,

the method characterized in that it further comprises the following steps;

generating an error measure (ERR) by quantifying the noise measured in said metric (Y) by the noise compensating mapping (NCM) using the first property set (C1) and the second property set (S2), and

generating an authentication decision (D) using said error measure (ERR).

10. A computer controlled method as claimed in claim 7, where the authentication decision is generated using information comprising the error measure (ERR) and a control value (V1) associated with both the reference object and the helper data (W1), to generate the authentication decision (D) identifying whether the physical object corresponds with the reference object.

11. A computer controlled method as claimed in claim 7, where the authentication method generates an interim decision (ID) by comparing information derived from the second property set (S1) with information derived from the control value (V1).

12. A computer controlled method as claimed in claim 11, where the authentication method comprises a step for comparing the error measure (ERR) with a predetermined threshold (t), and where information comprising the outcome of said comparison is used to accept or reject the interim decision (ID), resulting in a authentication decision (D) that is identical to, or different from the interim decision (ID) respectively.

13. A computer controlled method as claimed in claim 7, where the authentication method uses information comprising the error measure (ERR) and the first control value (V1) associated with both the reference object and the helper data (W1), to generate a probability measure (P) related to the authentication decision (D).

14. A computer controlled method as claimed in claim 7, where said noise compensating mapping (NCM) is an error correcting code decoder algorithm.

15. A computer controlled method as claimed in claim 7, further comprising a step to acquire an alleged identity.

16. A computer controlled method as claimed in claim 7, further comprising a step for generating a second control value (V2) by applying a one way function h on the second property set (S1), where said second control value (V2) is used to establish a sufficient match between the physical object and the reference object.

17. A computer controlled method of identification of a physical object using both a helper data (W1) and a control value (V1) associated with a reference object, the method according to claim 7, further comprising a step for establishing the identity of the physical object as being identical to that of the reference object.

18. A computer controlled method as claimed in claim 17, where the identification method uses information comprising the error measure (ERR) and the first control value (V1) associated with both the reference object and the helper data (W1), to generate a probability measure (P).

20. A computer program product comprising program code means stored on a computer readable medium for performing the method of claim 7 when said program product is run on a computer.

8. A computer controlled method as claimed in 7, where the step for generating the error measure (ERR) comprises quantifying the difference between the first property set (C1) and the second property set (S1).

9. A computer controlled method as claimed in 7, where the step for generating the error measure (ERR) comprises a step that:

generates a third property set (C2) using the noise robust mapping (NRM) on information comprising the second property set (S1),

generates the error measure (ERR) by quantifying the difference between the first property set (C1) and the third property set (C2).

19. A computer controlled method as claimed in 18, where said probability measure (P) is used to establish the identity of said physical object.