Fingerprint Interoperability Seminar

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The increasing use of distributed authentication architecture has highlighted interoperability issues of biometric systems. This presentation highlights the ongoing efforts at understanding fingerprint sensor interoperability. BSPA Labs has conducted experiments over the past few years aimed at addressing the challenges related to sensor interoperability. This presentation was given at a research seminar which covered the following: Importance of fingerprint sensor interoperability, sources of issues related to sensor interoperability, analysis framework for evaluating sensor interoperability, discussion of experimental results and its practical applicability

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Fingerprint Interoperability Seminar

  1. 1. Online Research Seminar: Fingerprint Sensor Interoperability Performance Shimon K. Modi, Ph.D. Shimon K Modi Ph D Director of Research, BSPA Labs Biometric Standards, Performance, and Assurance Laboratory | www.bspalabs.org Purdue University, Department of Industrial Technology
  2. 2. Agenda  Problem statement & Goal  Data collection protocol  Results C l i Conclusions and Future work d k Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  3. 3. Problem Definition Thermal Swipe Optical Touch Capacitive Touch  Matching fingerprints collected on different types  of sensors increases probability of false matches  of sensors increases probability of false matches and false non‐matches Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  4. 4. Goal Acquisition  Technology Interaction  Sensor  Type Characteristics Sensor  Interoperability  Create a statistical analysis framework to examine the  effect of sensor dependent distortions and variations  on False Non Match Rate (FNMR) of mixed datasets Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  5. 5. Sensor Description  8 commercially available fingerprint sensors 8 commercially available fingerprint sensors  1 thermal swipe sensor  1 capacitive  swipe sensor 4O i l 4 Optical touch sensors h  2 Capacitive touch sensors  All sensors were 500 dpi resolution Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  6. 6. Data Collection Protocol Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  7. 7. Description of Participants Total 190 Gender Male Female 131 59 Occupation Manual Laborer Office Worker 17 173 Age Groups < 30 years 30‐50 years > 50 years 156 23 11 Handedness Right Left  Ambidextrous 164 23 3 Ethnicity Caucasian Black Hispanic American  American Asian Other Indian 133 8 11 1 34 3 Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  8. 8. Data Analysis Methodology • VeriFinger extractor • Descriptive Stats Minutiae  Count • Test of similarity • NFIQ • Descriptive Stats Descriptive Stats Image  Quality • Test of Similarity • VeriFinger matcher • Native and mixed datasets FNMR  FNMR Matrix • Test of proportions Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  9. 9. VeriFinger 5.0 Extractor Minutiae Count Analysis Dataset n M SD S1 1140 41.72 10.57 S2 1122 28.32 10.73 S3 1140 40.25 10.12 S4 1128 30.74 8.06 S5 1110 24.38 6.87 S6 1134 38.62 9.18 S7 1134 27.53 7.69 S8 1122 26.15 6.73 Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  10. 10. Minutiae Count Analysis  Minutiae count extracted using VeriFinger g g Overall Hypothesis: H10: µi minutiae_count = µ2 minutiae_count……..= µ8 minutiae_count H1A: µi minutiae_count ≠ µ2 minutiae_count……..≠ µ8 minutiae_count : µ ≠ µ ≠ µ Pairwise Comparisons: H1’0: µi minutiae_count = µj minutiae_count for all i ≠ j H1’A: µi minutiae_count ≠ µj minutiae_count for all i ≠ j  Rejected null hypothesis H10  All pairs statistically significant at α = 0.05 p y g Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  11. 11. NFIQ Image Quality Analysis  Dataset N M Median S1 1140 1.29 1 S2 1122 1.91 2 S3 1140 1.75 1 S4 1128 2.00 2 S5 1110 2.18 2 S6 1134 1.77 2 S7 1134 2.03 2 S8 1122 1.58 2 Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  12. 12. Image Quality Analysis  Image quality computed using NFIQ g q y p g Q Overall Hypothesis: H20: µi qscore = µ2 qscore……= µ9 qscore : µ = µ =µ H2 A: µi qscore ≠ µ2 qscore…..≠ µ9 qscore Pairwise Comparisons: Pairwise Comparisons: H2’0: µi qscore = µj qscore for all i ≠ j H2’A: µi qscore ≠ µj qscore for all i ≠ j  NFIQ uses a 3‐layer feed forward nonlinear perceptron model  to predict the image quality values based on the input feature  vector of the fingerprint image  vector of the fingerprint image  Performed a Kruskal Wallis Test of similarity of ranks Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  13. 13. Tukey’s HSD Pairwise Test D2 D3 D4 D5 D6 D7 D8 D1 S S S S S S S D2 S S S S S S D3 S S S S S D4 S S NS S D5 S S S D6 S S D7 S  S‐ Significant difference S Significant difference  NS – Not Significant Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  14. 14. False Non Match Rate (FNMR) Matrix Analysis Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  15. 15. FNMR Matrix – Fixed FMR 0.1% TEST D1 D2 D3 D4 D5 D6 D7 D8 D1 0.47 6.79 5.60 4.44 5.61 31.03 11.30 3.76 D2 7.33 5.05 6.49 7.44 10.92 16.18 10.83 6.11 E N D3 8.57 4.78 0.24 1.07 1.28 2.73 1.79 0.54 R O D4 5.10 4.44 0.95 0 1.39 1.60 1.83 1.08 L D5 5 5.56 7.64 .6 0.85 0.85 0. 8 0.78 4.42 . 2.23 . 3 0. 0.42 D6 33.86 13.42 2.99 1.30 4.47 0.17 1.41 2.15 D7 14.92 9.89 1.73 2.12 2.35 2.71 0.94 1.85 D8 2.87 2.96 0.12 0.90 0.49 1.73 0.77 0.11 Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  16. 16. Test of Homogeneity of Proportions  FNMR Matrix is useful for numerical comparison p  Test of Homogeneity of FNMR was performed  between native FNMR and mixed FNMR b i d i d  Compare multiple proportions using Marascuillo Compare multiple proportions using Marascuillo Procedure Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  17. 17. Results D1 D2 D3 D4 D5 D6 D7 D8 D1 S S S S S S S D2 NS NS NS S S S NS D3 S S NS NS S S NS D4 S S NS NS S S NS D5 S S NS NS S NS NS D6 S S S NS S NS NS D7 S S NS NS NS NS NS D8 S S NS NS NS S NS  S‐ Significant difference S Significant difference  NS – Not Significant Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  18. 18. Impact of Quality on Interoperable FNMR  Removal of low quality images will reduce FNMR  q y g of native and interoperable datasets   But will it make the FNMR of mixed datasets  similar to FNMR of native datasets? i il f i d ?  FNMR matrix regenerated using VeriFinger 5 0 for FNMR matrix regenerated using VeriFinger 5.0 for  datasets comprised of images with NFIQ score of  1, 2, or 3   The test of homogeneity of proportions was rerun  on native and mixed dataset FNMR Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  19. 19. Results D1 D2 D3 D4 D5 D6 D7 D8 D1 S S S S S S S D2 NS NS NS S S NS NS D3 S S NS NS S S NS D4 S S NS NS S S NS D5 S S NS NS S NS NS D6 S S S S S S S D7 S S NS NS NS NS NS D8 S S NS NS NS S NS  S Significant difference S‐ Significant difference  NS – Not Significant Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  20. 20. Consistency of Placement  Interaction with different sensors can lead to inconsistent placement  due to sensor design, interaction type, capture area  Metric of consistency – detection of core Core detected in both images Core detected in one image Samples from different sensors – Samples from different sensors – Correctly Verified Incorrectly rejected Core detected in none of the images Samples from same sensor – Correctly verified Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  21. 21. Consistency of Placement  Interaction with different sensors can lead to  inconsistent placement due to sensor design,  interaction type, capture area  Metric of consistency detection of core Metric of consistency – detection of core D1,D6 D6,D1 Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  22. 22. Interoperability ‐ Interaction & Technology  A l d i t Analyzed interoperability of datasets separated by  bilit f d t t t db interaction and technology type  Created three datasets 1. Fingerprints collected from swipe sensors g p p 2. Fingerprints collected from optical touch sensors 3. Fingerprints collected from capacitive touch sensors g p p Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  23. 23. Interoperability ‐ Interaction & Technology  TEST Swipe p Optical  Capacitive  p p Touch Touch E Swipe 4.81 11.75 6.58 N R O Optical  11.93 1.53 1.89 L Touch Capacitive  4.76 1.47 0.45 Touch Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  24. 24. Conclusions  No technology specific impact on similarity of  gy p p y minutiae count and quality score  Image quality analysis reduced FNMR, but did not  increase similarity of FNMR i i il i f  Consistency of placement improved performance  of mixed datasets of mixed datasets  Data collected from same interaction type had  lower FNMR Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  25. 25. Future Work  Apply the analysis framework to test False Match  pp y y Rates (FMR)  Removing low quality images on interoperable  datasets did not make FNMR more similar d did k i il  Analyze which factors are affected by removal of low quality images  Further analysis into skin characteristics and its Further analysis into skin characteristics and its  impact on image quality  Develop a detailed ridge spacing profile which can  be used as a sensor agnostic method for  transforming images and compensate for  El ti D f Elastic Deformation ti  Capture mechanisms Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  26. 26. Upcoming Research Seminar  Paper presented at BTAS 2009- 2009 http://www.bspalabs.org/files/185/BTAS%20Fprint%20Se nsor%20Interop.pdf  Slides available at http://www.bspalabs.org/presentations-on- demand  Next seminar will be on Human Biometric Sensor Interaction – Dr Eric Kukula Dr.  Feb 10th at 3:30pm (EST)  Sign up at http://www.bspalabs.org/archives/1236 Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org
  27. 27. Thank You! Questions… Contact Information: Shimon K. Modi, Ph.D. modis@purdue.edu BSPA L b Laboratory | www.bspalabs.org b l b Purdue University, Knoy Hall of Technology 401 North Grant Street West Lafayette, IN 47907 2021 47907-2021 Phone: (765) 494-2311 Fax: (765) 496-2700

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