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Examining Intra-Visit Iris Stability - Visit 6

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In this research, intra-visit match score stability was examined for the human iris. Scores were found to be statistically stable in this short time frame.

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Examining Intra-Visit Iris Stability - Visit 6

  1. 1. Patrick Herrmann, Kautilya Madhav, Catherine Muturi, Jack Rosati, Curtis Rose, Jonathan Ruggaard, Ryan Rumble, Kyle Senteney, Ben Petry, Steve Elliott, and Kevin Chan EXAMINING INTRA-VISIT IRIS STABILITY (VISIT 6)
  2. 2. • How to Identify a Person • Biometrics • What is it? • Types? • Why Care? • Iris Recognition • What is it? • How Recognition Works • Stability • Other research • Research Question INTRODUCTION OVERVIEW
  3. 3. •Identity can be verified in three ways: •What someone knows: Secret Knowledge •What someone has: Token •What someone is: Biometrics HOW TO IDENTIFY A PERSON
  4. 4. •Biometrics: “a measurable, physical characteristic or biological characteristic used to recognize the identity or verify these claimed identity of an enrollee” [1] WHAT IS BIOMETRICS?
  5. 5. •Large scale •Not easily stolen or imitated •Difficult to manipulate ADVANTAGES OF BIOMETRICS
  6. 6. BIOMETRICS • Physiological • Hand geometry • Fingerprint • Iris Recognition • Behavioral • Signature • Keystroke • Voice
  7. 7. •This study will examine stability of match scores from samples taken within a time frame of 10 minutes or less. RESEARCH QUESTION
  8. 8. •Controls the amount of light reaching the retina •Captured using near infrared light IRIS FUNCTION
  9. 9. •Depending on time window of sample collection (10 min) •IREX VI found stability [5] •Notre Dame study found evidence of aging [6] TIME CHANGE
  10. 10. •“Iris remains stable over time” [2] •“Does not remain stable over time” [3] •“Performance scores affected by time separation” [4] IRIS AGING / STABILITY RESEARCH
  11. 11. EFFECT ON THE EYE Short Term •Dilation •Lighting •Environment •Glare Long Term •Muscular Degeneration •Damage to iris •Cataracts •Disease
  12. 12. •Consistency between samples of the individuals DEFINE STABILITY OF THE IRIS 𝑺. 𝑺. 𝑰𝒊 = 𝒙 𝒊 𝟐 − 𝒙 𝒊 𝟏 𝟐 + 𝒚 𝒊 𝟐 −𝒚 𝒊 𝟏 𝟐 𝒙 𝒎𝒂𝒙− 𝒙 𝒎𝒊𝒏 𝟐 + 𝒚 𝒎𝒂𝒙−𝒚 𝒎𝒊𝒏 𝟐 [7]
  13. 13. •Established in 2013 by O’Connor [7] •Initial work in fingerprints, but research continues in iris and face •Another way of examining the performance of a user STABILITY SCORE INDEX
  14. 14. METHODOLOGY
  15. 15. • Data used was from a 2012 ICBR data collection captured at Purdue University • Examined data runs in depth, created groupings and created data sets of each grouping • Used Megamatcher to analyze the data runs • Stability score index created for each subject OVERVIEW
  16. 16. • Data collection began on 11 June 2010 and lasted for 1 year and 2 days (2010-06-11Z/P1Y0M0W2D). • The time scope of interest for this report is in the day range. • The collection period of interest for this analysis began on 11 April 2013 and lasted for four weeks and 1 day (2013-04-11Z/P0Y0M4W1D). COLLECTION PERIOD
  17. 17. • Identify any error for each subject and iris from data runs • Subjects with incorrect number of images, further investigated • If the number of subjects images were less than required amount of images then they were eliminated • The first data run was completely eliminated due to lack of images EXAMINED DATA RUNS IN DEPTH
  18. 18. • Subjects were narrowed down to only those that met testing requirements • These subjects were then pooled into new datasets • This included locator number, subject ID, and modality subtype EXPORTING REQUIRED SUBJECTS
  19. 19. • Newly created data sets were then arranged into grouping based on the subject and left or right iris • Each subject has 4 groupings of 6 images per group, the images were organized in order of three consecutive lefts and then three consecutive rights CREATED GROUPING FOR EACH IRIS AND EACH VISIT
  20. 20. • Each grouping was put into its own data set • Group 1 of all subjects were combined into one data set • Done for all four groupings SPLIT GROUPS INTO SEPARATE DATA RUNS
  21. 21. • The data runs were then processed by Megamatcher and exhaustively matched against all other images • Megamatcher then outputs a genuine and impostor score for each subject MEGAMATCHER USED TO ANALYZE DATA
  22. 22. • Stability score index (SSI) is made possible by taking the Euclidian distance of an individual subject which determines the change in location within the menagerie across two data runs • The SSI ranges from 0-1, zero being the most stable score, it allows for stability of subjects to be determined and quantified CREATION OF STABILITY SCORE INDEX
  23. 23. RESULTS
  24. 24. VISIT 6 AGE GROUPS
  25. 25. VISIT 6 GENDER
  26. 26. VISIT 6 – SELF DISCLOSED ETHNICITY
  27. 27. •Grouping 1-V6 (1-2, 1-3, 1-4) •Grouping 2-V6 (2-1, 2-3, 2-4) •Grouping 3-V6 (3-1, 3-2, 3-4) •Grouping 4-V6 (4-1, 4-2, 4-3) DATA ANALYSIS
  28. 28. GROUPING 1 – V6 ANALYSIS There was not a statistically significant difference between the median stability scores between different groupings (H(2) = 0.05, p = 0.976), with a mean rank of 91.7 for 1-2, 89.9 for 1-3, and 89.9 for 1-4.
  29. 29. GROUPING 2 – V6 ANALYSIS There was a statistically significant difference between the median stability scores between different groupings (H(2) = 6.24, p = 0.044), with a mean rank of 104.2 for 2-1, 84.3 for 2-3, and 83.0 for 2-4.
  30. 30. GROUPING 3 – V6 ANALYSIS There was not a statistically significant difference between the median stability scores between different groupings (H(2) = 3.83, p = 0.148), with a mean rank of 101.0 for 3-1, 83.3 for 3-2, and 87.2 for 3-4.
  31. 31. GROUPING 4 – V6 ANALYSIS There was not a statistically significant difference between the median stability scores between different groupings (H(2) = 5.77, p = 0.056), with a mean rank of 103.3 for 4-1, 81.3 for 4-2, and 87.0 for 4-1.
  32. 32. VISIT 1 N H DF P Group 1 60 0.05 2 0.976 Group 2 60 6.24 2 0.044 Group 3 60 3.83 2 0.148 Group 4 60 5.77 2 0.056 RESULTS
  33. 33. CONCLUSIONS
  34. 34. •H0: The median stability scores are equal •Ha: The median stability scores are not equal •α = 0.05 HYPOTHESIS
  35. 35. •There was not a statistically significant difference between the median of the groupings, as indicated in the summary table. For this data, we can conclude that the iris is stable in this visit, even though the second grouping shows significant difference. RESULTS SUMMARY
  36. 36. •More research to be conducted to validate the stability of the iris over a longer period of time (weeks, months, years) •Re-examine datasets that rejected the null hypothesis FUTURE WORK
  37. 37. • [1] Association of Biometrics, 1999, p. 2 • [2] Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 21–30. doi:10.1109/TCSVT.2003.818350 • [3] Baker, S. E., Bowyer, K. W., Flynn, P. J., & Phillips, P. J. (2013). Template Aging in Iris Biometrics : Evidence of Increased False Reject Rate in ICE 2006. In Handbook of Iris Recognition, 205–218, London: Springer. • [4] Tome-Gonzalez, P., Alonso-Fernandez, F., & Ortega-Garcia, J. (2008). On the Effects of Time Variability in Iris Recognition. Biometrics: Theory, Applications, and Systems, 2008. 2nd IEEE International Conference, 1–6. • [5] Grother, P., Matey, J. R., Tabassi, E., Quinn, G. W., & Chumakov, M. (2013). IREX VI. Temporal Stability of Iris Recognition Accuracy. NIST Interagency Report, 7948, 1-3. • [6] Baker, S. E., Bowyer, K. W., & Flynn, P. J. (2009). Empirical evidence for correct iris match score degradation with increased time-lapse between gallery and probe matches. In Advances in Biometrics (pp. 1170-1179). Springer Berlin Heidelberg. • [7] O’Connor, K. J. (2013). Examination of stability in fingerprint recognition across force levels. Purdue University, West Lafayette, Indiana. REFERENCES

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