Fingerprint identification system for access control

• US 20070248249A1
• Filed: 04/20/2006
• Published: 10/25/2007
• Est. Priority Date: 04/20/2006
• Status: Abandoned Application

• Current Assignee: Bioscrypt, Inc. (Advent International Corporation)
• Original Assignee: Bioscrypt, Inc. (Advent International Corporation)
• Inventors: Stoianov, Alexei
• Application Number:
  US11/408,094
• Publication Number:
  US 20070248249A1
• Time in Patent Office: Days
• US Class Current: 382/124
• CPC Class Codes: G06V 40/1365 Matching; Classification

Abstract

A one-to-many identification system for access control allows a search rate of up to ˜1:30,000 in a real time. The system uses a very fast pattern based screening algorithm followed by a fast minutiae based screening algorithm. A fused score of both algorithms is used as a decision metric to screen out a vast majority of all the templates after the second stage. The remaining templates are sent to a full minutiae based algorithm to obtain a minutiae comparison score. If the result is still inconclusive after the third stage, a full pattern based algorithm is run, and its score is fused with the minutiae comparison score. The system also uses an adaptive classification technique which minimizes a distance between each template and a number of templates. The system can be realised as a standalone unit or on a server.

Claims

1. A method of biometric identification, comprising:

for each biometric template in a first universe of templates, determining a first metric of similarity between each first universe template and a candidate biometric;

based on determined first metrics of similarity, selectively accepting or rejecting said each first universe template as a possible match for said candidate biometric to thereby accept a second universe of templates, said second universe of templates being a sub-set of said first universe of templates;

for each second universe template, determining a second metric of similarity between said each second universe template and said candidate biometric;

determining a composite metric of similarity based on said first metric of similarity for said each second universe template and said second metric of similarity for said each second universe template.

2. The method of claim 1 further comprising:

based on determined composite metrics of similarity, selectively accepting or rejecting said each second universe template as a possible match for said candidate biometric to thereby accept a third universe of templates, said third universe of templates being a sub-set of said second universe of templates.

3. The method of claim 2 wherein said first metric of similarity is, at least in part, a measure of similarity between a translation invariant biometric feature vector representation of said each first universe template and a translation invariant biometric feature vector representation of said candidate biometric.

4. The method of claim 3 wherein said first metric of similarity is at least substantially orthogonal to said second metric of similarity.

5. The method of claim 3 wherein said translation invariant biometric feature vector representation of said each first universe template is a Fourier intensity representation and wherein said translation invariant biometric feature vector representation of said candidate biometric is a Fourier intensity representation.

6. The method of claim 3 wherein said translation invariant biometric feature vector representation of said each first universe template is a gradient magnitude representation linked to an alignment feature and wherein said translation invariant biometric feature vector representation of said candidate biometric is a gradient magnitude representation linked to an alignment feature.

7. The method of claim 3 wherein said translation invariant biometric feature vector representation of said each first universe template is a gradient direction representation linked to an alignment feature and wherein said translation invariant biometric feature vector representation of said candidate biometric is a gradient direction representation linked to an alignment feature.

8. The method of claim 5 wherein said first metric of similarity is also based on a metric of similarity between a gradient magnitude representation of said each first universe template linked to an alignment feature and a gradient magnitude representation of said candidate biometric linked to an alignment feature.

9. The method of claim 8 wherein said first metric of similarity is also based on a metric of similarity between a gradient direction representation of said each first universe template linked to an alignment feature and a gradient direction representation of said candidate biometric linked to an alignment feature.

10. The method of claim 9 wherein said gradient magnitude of said candidate biometric and said gradient direction of said candidate biometric are obtained at pre-selected points relative to said alignment feature.

11. The method of claim 10 wherein said candidate biometric is a fingerprint and each said alignment feature is a core or delta of said fingerprint.

12. The method of claim 1 wherein said second universe of templates has a pre-determined number of templates and wherein said selectively accepting or rejecting said each first universe template as a possible match for said candidate biometric to thereby accept said second universe of templates comprises accepting first universe templates until said pre-determined number of templates is reached.

13. The method of claim 3 wherein said translation invariant biometric feature vector representation of said each first universe template comprises a set of two-dimensional locations and wherein said translation invariant biometric feature vector of said candidate biometric comprises a value of a Fourier Transform intensity of said candidate biometric at each location of said set of two-dimensional locations.

14. The method of claim 13 wherein said first metric of similarity comprises a sum of each said value.

15. The method of claim 13 wherein said Fourier Transform intensity of said candidate biometric is a randomized Fourier Transform intensity.

16. The method of claim 5 further comprising obtaining said Fourier intensity representation of said candidate biometric as follows:

obtaining a two-dimensional representation of a Fourier Transform intensity from said candidate biometric;

for each area of a plurality of areas spanning pre-selected Fourier frequencies, obtaining a value representative of said area so as to obtain a set of values, said set of values comprising said Fourier intensity representation of said candidate biometric.

17. The method of claim 5 further comprising obtaining said Fourier intensity representation of said candidate biometric as follows:

obtaining a two-dimensional representation of a Fourier Transform intensity from a candidate biometric image;

obtaining a circular harmonic expansion of said Fourier Transform intensity;

obtaining a representation of magnitude of a pre-determined number of lowest order circular harmonics so as to obtain a set of values, said set of values comprising said Fourier intensity representation of said candidate biometric.

18. The method of claim 1 wherein said determining said composite metric of similarity comprises:

retrieving parameters defining straight line segments and deriving said composite metric of similarity from said first metric of similarity, said second metric of similarity, and said parameters.

19. The method of claim 18 wherein said straight line segments are derived as follows:

for each of a plurality of authorized biometrics, deriving a template;

for each of a plurality of candidate biometrics, each candidate biometric being either one of said authorized biometrics or an unauthorized biometric;

for each said template;

obtaining said first metric of similarity between said each candidate and said template;

obtaining said second metric of similarity between said each candidate and said template;

plotting said first metric of similarity and said second metric of similarity as a point on a Cartesian plot;

bisecting said plot with said straight line segments such that said plot is bisected into a region dominated by points representative of metrics of similarity between templates and candidate biometrics from which said templates were derived and a region dominated by points representative of metrics of similarity between templates and candidate biometrics which are other than candidate biometrics from which said templates were derived.

20. The method of claim 19 wherein each straight line segment is defined by ax+by +c=0 and said composite metric of similarity is determined from parameters for at least one of said straight line segments as ax+by +c where x is said first metric of similarity and y is said second metric of similarity.

21. The method of claim 1 further comprising:

for each template in one of said first universe of templates and said second universe of templates, obtaining a template characteristic vector;

for said candidate biometric, obtaining a candidate characteristic vector;

determining a distance between said candidate biometric and said each template based on said template characteristic vector and said candidate characteristic vector;

obtaining a list of selected templates such that each selected template has a lower distance from said candidate biometric than any template which is not a selected template;

for each of said selected templates, comparing said list of selected templates with a list of neighbour templates associated with each selected template to obtain a further metric of similarity between said candidate biometric and said each selected template.

22. The method of claim 21 wherein said further metric of similarity comprises a degree of overlap between said list of selected templates and said list of neighbour templates.

23. The method of claim 21 wherein said each template is in said first universe of templates and wherein each said first metric of similarity is, at least in part, a measure of similarity between said candidate characteristic vector and one said template characteristic vector.

24. The method of claim 23 wherein each said first metric of similarity is further derived from said further metric of similarity.

25. The method of claim 24 wherein said candidate characteristic vector is a translation invariant biometric feature vector representation of said candidate biometric and each said template characteristic vector is a translation invariant biometric feature vector representation of said each first universe template.

26. The method of claim 1 wherein said candidate biometric is a pixelated candidate image and wherein said determining a second metric of similarity between said each second universe template and said pixelated candidate image comprises:

determining a pre-defined fiducial point in said pixelated candidate image;

extracting a plurality of rectangular arrays of pixels from said pixelated candidate image, each rectangular array having a pre-defined location with respect to said fiducial point in said pixelated candidate image;

comparing values at pre-selected points of at least some of said rectangular arrays of pixels with values at corresponding pre-selected points stored in respect of rectangular arrays previously extracted from said each second universe template.

27. A biometric identification device, comprising:

a biometric sensor for obtaining a candidate biometric;

a memory storing a first universe of biometric templates;

a controller operable to;

for each biometric template in said first universe of biometric templates, determine a first metric of similarity between each first universe template and said candidate biometric;

based on determined first metrics of similarity, selectively accept or reject said each first universe template as a possible match for said candidate biometric to thereby accept a second universe of templates, said second universe of templates being a sub-set of said first universe of templates;

for each second universe template, determine a second metric of similarity between said each second universe template and said candidate biometric;

determine a third metric of similarity between said each second universe template and said candidate biometric, said third metric of similarity based on said first metric of similarity for said each second universe template and said second metric of similarity for said each second universe template.

28. A method to facilitate one-to-many biometric identification, comprising:

for each biometric of a plurality of biometrics, obtaining a template comprising a characteristic vector representing said each biometric;

determining a distance between each pair of templates based on each said characteristic vector;

based on distance determinations between each pair of templates, for said each template determining nearest neighbour templates;

augmenting said each template with a list of said nearest neighbour templates.

29. The method of claim 28 further comprising further augmenting said each template with said list of nearest neighbour templates associated with each of said nearest neighbour templates.

30. A method of one-to-many biometric identification, comprising:

for each template in a universe of templates obtaining a template characteristic vector;

for said candidate biometric, obtaining a candidate characteristic vector;

determining a distance between said candidate biometric and said each template based on said template characteristic vector and said candidate characteristic vector;

obtaining a list of selected templates such that each selected template has a lower distance from said candidate biometric than any template which is not a selected template;

for each of said selected templates, comparing said list of selected templates with a list of neighbour templates associated with each selected template to obtain a metric of similarity between said candidate biometric and said each selected template.

31. The method of claim 30 wherein said metric of similarity comprises a degree of overlap between said list of selected templates and said list of neighbour templates.

32. The method of claim 30 further comprising obtaining said list of neighbour templates associated with said each selected template by:

determining a distance between each pair of templates based on said template characteristic vector;

for each template, selecting said list of neighbour templates such that each neighbour template has a lower distance from said each template than any template which is not a neighbour template.

33. The method of claim 32 wherein said metric of similarity is a classification metric and further comprising determining a further metric of similarity between a candidate biometric and said each template based on said candidate characteristic vector and each said template characteristic vector and fusing said classification metric with said further metric to obtain a composite metric of similarity.