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Indexing and Binning Large Databases

Indexing and Binning Large Databases. Abstract. Problems with large databases Biometric identification (1:N Matching) does not scale well with size No established way to organize high dimensional biometric data Proposed Solution Reduce search space before 1:N matching

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Indexing and Binning Large Databases

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  1. Indexing and Binning Large Databases

  2. Abstract • Problems with large databases • Biometric identification (1:N Matching) does not scale well with size • No established way to organize high dimensional biometric data • Proposed Solution • Reduce search space before 1:N matching • Divide the database using Clustering Techniques • Contributions • We analyze the effect of implementing a binning scheme on search performance and accuracy • We present binning and pruning approaches using multiple biometrics • Using hand geometry and signature, we have achieved a search space reduction of 95% without any FRR

  3. Background • Only biometric identification (1:N matching) can prevent duplicate enrollments, double dipping • Biometrics are being deployed for immigration and national ID applications • US-VISIT program • Voter ID and national ID programs[3] • Potential size that can run into millions • Current research is focused only on accuracy • Apart from accuracy, scalability, speed and efficiency also become important at this scale

  4. Textual/Numeric Data Data is scalar(1D) Textual/numeric data can be linearly ordered and therefore easily indexed Biometric Data Biometric templates are high dimensional No linear ordering or sorting methods exists for biometric data Challenges

  5. Search space analysis • As number of stored templates increases, template density (TD) also increases

  6. Identification problem • Number of false positives grows geometrically with the size of the database • Let FAR and FRR be the False Acceptance Rate (probability) and False Reject Rate (probability) for 1:1 matching • For a 1:N matching, • The total number of False Accepts is given by

  7. State of the Art

  8. State of the Art

  9. Identification problem (contd.) • Even if FAR = 0.0001%, False accepts = 1 in 10 for N=100000(lower bound) in the identification case. • No single biometric is capable of meeting this security requirement individually • Ways to reduce identification errors: • Reduce FAR • FAR is limited by feature representation and the recognition algorithm • Cannot be indefinitely reduced • Reduce N • Classify or index the biometric database. (e.g Henry classification system for fingerprints) • Index the records based on meta-data • Can we do better?

  10. Fingerprint Features Fingerprints can be classified based on the ridge flow pattern Fingerprints can be distinguished based on the ridge characteristics 65% of fingerprints belong to the Loop class

  11. Henry Classification of Fingerprints • [Ratha et al,1996] used Henry Classification on database of 1800 templates, tested on 100 templates • Search Space: 25%; FRR: 10% • [Jain, Pankanti,2000] similar experiment on database of 700 templates achieved FRR: 7.4% (Focus on classification only) • State-of-art Fingerprint classification system [Capelli,Maio,Maltoni,Nanni,2003] has FRR 4.8% for 5 class problem and 3.7% for 4 class problem • Though natural class exists, still classification is non-trivial • Natural classes do not exist for biometrics like Hand Geometry • Need more sophistication for partitioning database

  12. Analysis of search space reduction • We can improve performance by reducing the search space during identification • Let PSYS – Penetration rate [between 0.0 and 1.0] • Penetration rate is the average fraction of the database searched during identification • Effective size = N*PSYS • For a 1:N matching, • The total number of False Accepts is given by • State of the art fingerprint systems has PSYS=0.5

  13. Effect of binning on accuracy • For PSYS < 0.2, the false accepts are almost constant • Query response time improves by a factor of PSYS • Capabilities of a low FAR system • Will allow us to screen immigrants at airports • Will make biometric systems more user-friendly by eliminating the need to remember PINs and IDs

  14. Binning • Binning can be used to achieve a smaller PSYS • Partition the feature space • Each bin is represented by a cluster center CK • Records are compared with only NB cluster centers • Bin representatives are computed offline during training • Challenges • How to handle clustering of large databases? • How to handle additions and deletions?

  15. Tradeoff • Although binning reduces search space, it introduces another source of identification error : Bin Miss • If the bin in which the user record exists is not searched, then FRR is generated no matter how good the matcher is • If P(B) is the probability of getting the correct bin • Binning increases the probability of False Rejects • Not tolerable in security and screening applications • Solution: • Use K-means clustering to find K bins • Check Nsnearest bins for the record, such that P(B) = 1

  16. Formal definition of Binning • In general a biometric template may be represented as a vector • Vectors are represented into N distinct clusters; each represented by a ‘code book vector’ • The code book vectors divide the feature space into N distinct Voronoi regions • Every template is closest to the mean (codebook vector) of the region it belongs to

  17. Search Space Partition: Voronoi Regions

  18. Hand Geometry Template • Feature extraction stages • Image capture • Binarization • Contour Extraction • Noise Removal • 35 Features are extracted • 25 directly measured features • 10 ratio and perimeter features

  19. Signature Template 11 Features Extracted • Regression Constants b0,b1 • Compactness • Signature Length • Major Stroke Length • Major Stroke Angle • Connected Components • Hole Count • Hole Area • Stroke Count • Signing Time

  20. Results 35 – Dimensional Hand Geometry data Best Penetration: 35.8% for 6 bins FRR = 0% 11 – Dimensional Signature data Best Penetration: 35.57% for 6 bins FRR = 0% Dataset 250 Training Set & 250 Testing Set

  21. Multi-modal approach • Resulting bins have very high template densities • A different biometric modality should be used to classify templates within a bin • Multimodal biometrics • Using multiple biometrics improves accuracy • It is difficult to forge multiple biometrics • Composite templates reduce template density • Statistical independence ensures that individual binning results are diverse • The search space (intersection of bins) is reduced due to low commonality between the individual binning results

  22. Multi-Modal Approach

  23. Multi-Modal Approach • Search Space: 5% original database size; FRR – 0%

  24. Results of Combination Best combined penetration rate of 5% Dataset 250 Training Set & 250 Testing Set

  25. Binning v/s Indexing • Applications can have frequent insertions of new templates • Binning works well when database is static • Insertions will require re-partitioning the entire database • Indexing can be used in both – static and dynamic database scenarios • Trees are commonly used for indexing • Extend the concept of indexing relational databases to indexing biometric databases • Much more challenging – no concept of primary key exists in biometric templates!

  26. Pyramid Technique spatial hashing • Determine the Pyramid (i) within with which the template lies • Determine height (h) of template from the apex • The 1-D value = Pyramid Number (i) + Height (h) • Indexing done using B+ Trees

  27. Various Indexing Techniques Grid Files KD Tree R Tree R+ Tree X Tree Pyramid Technique

  28. Comparative Study

  29. Results of Indexing 35 – Dimensional Hand Geometry data Best Penetration: 27% FRR = 0% Dataset 450 Training Set & 450 Testing Set • Parallel combination with signature will further reduce the search space

  30. Multimodal Biometrics

  31. 2D Biometric: Signature & Fingerprint Fusion Impostor Score Pairs True Match Score Pairs

  32. Accuracy (1-FRR) Optimal Fusion AlgorithmSignature Fused With Fingerprint Unrealizable Performance Area True Match Score Pairs Optimal Fusion ROC Fusion Algorithm False Accept Rate (FAR) Suboptimal Performance Area Impostor Score Pairs The ROC is the boundary between what is possible and suboptimal performance.

  33. No-Match Zone Match Zone Optimal Fusion Algorithm Decision Regions99.04% Accuracy @ Specified FAR of 1 in a Million 2nd Biometric Score Axis 1st Biometric Score Axis irregular decision region boundary due to finite sample size the more data the smoother the boundaries

  34. Accuracy (1-FRR) RSS Fusion Algorithm for Fingerprint & SignatureProvides A Suboptimal Performance ROC Optimal ROC True Match Score Pairs RSS Fusion ROC RSS Fusion False Accept Rate (FAR) Impostor Score Pairs

  35. No-Match Zone Match Zone RSS Fusion Decision Regions96.11% Accuracy @ Specified FAR of 1 in a Million 2nd Biometric Score Axis 1st Biometric Score Axis

  36. Accuracy (1-FRR) OR Fusion Algorithm for Fingerprint & SignatureProvides A Suboptimal Performance ROC Optimal ROC True Match Score Pairs OR Fusion ROC OR Fusion False Accept Rate (FAR) Impostor Score Pairs

  37. No-Match Zone Match Zone OR Fusion Decision Regions96.85% Accuracy @ Specified FAR of 1 in a Million 2nd Biometric Score Axis 1st Biometric Score Axis

  38. Accuracy (1-FRR) AND Fusion Algorithm for Fingerprint & SignatureProvides A Suboptimal Performance ROC Optimal ROC True Match Score Pairs AND Fusion ROC AND Fusion False Accept Rate (FAR) Impostor Score Pairs

  39. No-Match Zone Match Zone AND Fusion Decision Regions62.91% Accuracy @ Specified FAR of 1 in a Million 2nd Biometric Score Axis 1st Biometric Score Axis

  40. ROC

  41. Thank You

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