Biometric signatures and identification through the use of projective invariants

• US 7,623,685 B2
• Filed: 08/19/2005
• Issued: 11/24/2009
• Est. Priority Date: 08/20/2004
• Status: Expired due to Fees

• Current Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Original Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Inventors: Zheng, Gang, Wang, Chia-Jiu, Boult, Terrance Edward
• Primary Examiner(s): Bella; Matthew C.
• Assistant Examiner(s): PERUNGAVOOR, SATHYANARAYA V
• Application Number:
  US11/659,640
• Publication Number:
  US 20070274574A1
• Time in Patent Office: 1,558 Days
• Field of Search: 382/115
• US Class Current: 382/115
• CPC Class Codes: G06V 40/107 Static hand or arm

Abstract

Techniques, systems and methods for obtaining biometric signatures and identification are described. Broadly stated, embodiments of the present invention utilize specified geometric principles to provide means for accurate biometric identification using projective invariant features of a subregion of the human body. The present invention provides a means for computing biometric signatures and identification that are projective invariant and hence are not impacted by the viewing angle of the subregion of the human body containing the biometric data. This novel invention removes the restriction, often implicit in the previous work, of the imaging or sensing system being in a fixed repeatable (and generally orthogonal) viewing position. This invention can be applied across a wide range of biometrics, although it is most easily applicable to features that are approximately co-planar. A plurality of such projective invariant features can be used to define a biometric signature to either verify an individual'"'"'s identity, or recognize an individual from a database of already known persons.

Claims

1. A method of generating biometric signatures by a biometric signature system having a processor that executes the following steps, comprising the steps of:

a) capturing an image of a hand;

b) detecting a plurality of image features of a sub-region of the image, wherein the image features are points on creases of fingers of the hand;

c) computing a quasi projective invariant based on a subset of the plurality of image features using cross-ratios, wherein the quasi projective invariant is a property or measurement that is resilient to rotation, translation, uniform scaling, non-uniform scaling, shear and perspective projection; and

d) determining a similarity between the quasi projective invariant and a stored quasi projective invariant.

2. The method of claim 1, further including the steps of:

c1) computing a plurality of quasi invariants, each of the plurality of quasi invariants based on another subset of the plurality of image features.

3. The method of claim 1, further including the steps of:

d1) determining a similarity metric for the quasi projective invariant.

4. The method of claim 1, wherein step (c) further includes the step of:

c1) computing a plurality of projections of a curved feature, wherein the quasi projective invariant is obtained as a plurality of coefficients of a canonical functional representation of at least one the plurality of projections.

5. The method of claim 1, further including the steps of:

c1) detecting a second plurality of image features of a second sub-region;

d1) computing a second quasi invariant based on a subset of the second plurality of features.

6. The method of claim 5, further including the step of:

e) combining the quasi projective invariant and the second quasi invariant to form a biometric signature.

7. A biometric signature system, comprising:

an imaging system capturing an image of a hand;

a feature capturing system capturing a plurality of features from the image, wherein the features are points on creases of fingers of the hand;

a process computing quasi invariants receiving the plurality of features and computing a quasi projective invariant using cross-ratios, wherein a quasi projective invariant is a property or measurement that is resilient to rotation, translation, uniform scaling, non-uniform scaling, shear and perspective projection; and

a similarity computing process receiving the quasi projective invariant having an output of a score.

8. The system of claim 7, further including a biometric signature store coupled to the similarity computing process.

9. The system of claim 8, wherein the similarity computing process determines a similarity between a stored quasi projective invariant and the quasi projective invariant.

10. The system of claim 7, wherein the feature capturing system is part of the imaging system.

11. The system of claim 7, wherein the process computing quasi invariants computes a plurality of quasi invariants to form a biometric signature.

12. The system of claim 7, wherein the feature capturing system captures a first plurality of features from a first sub-region and captures a second plurality of features from a second sub-region.

13. A biometric signature method implemented by a biometric signature system having a processor that executes the following steps, comprising the steps of:

a) capturing an image of a hand;

b) detecting a plurality of image features from the image, wherein the image features are points on creases of fingers of the hand;

c) calculating a projective invariant from a subset of the plurality of features using cross-ratios, wherein a quasi projective invariant is a property or measurement that is resilient to rotation, translation, uniform scaling, non-uniform scaling, shear and perspective projection; and

d) determining a similarity between the projective invariant and a stored projective invariant.

14. The method of claim 13, wherein step (a) further includes the steps of:

a1) enrolling the human to form the stored projective invariant.

15. The method of claim 13, wherein step (c) further includes the step of:

c1) determining a plurality of quasi invariants from at least a portion of the plurality of features to form a biometric signature.

16. The method of claim 13, wherein step (d) further includes the step of:

d1) comparing a biometric signature to a stored biometric signature.

17. The method of claim 13, wherein step (c) further includes the step of;

c1) computing a plurality of projections of a curved feature, wherein the quasi projective invariant is obtained as a plurality of coefficients of a canonical functional representation of at least one the plurality of projections.