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CS 691 - Team 5

Biometric Authentication System. CS 691 - Team 5. Alex Wong Raheel Khan Rumeiz Hasseem Swati Bharati. Project Objectives. Develop a biometric authentication system Application coded in Java Determine the feasibility of the Dichotomy Model

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CS 691 - Team 5

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  1. Biometric Authentication System CS 691 - Team 5 Alex Wong Raheel Khan Rumeiz Hasseem Swati Bharati

  2. Project Objectives • Develop a biometric authentication system • Application coded in Java • Determine the feasibility of the Dichotomy Model • Report results using standard authentication system performance statistics CS 691 - Team 5 Biometric Authentication System

  3. Dichotomy Model • A statistically inferable approach to establishing the individuality of a biometric • Classifies two biometric samples as coming either from the same person (intra-variation) or from two different people (inter-variation) • Uses distance measure between two samples of the same class and between those of two different classes CS 691 - Team 5 Biometric Authentication System

  4. Objective of Dichotomy Model • Validation of individuality of biometric data statistically • Not the detection of differences of specific instances • Find the individuality of the entire population based on the individuality of a sample of n people, where n is much less than the population. • Allows inferential classification of individuals where large classes are involved and the whole population is not available for sampling CS 691 - Team 5 Biometric Authentication System

  5. Binary decision, yes/no Authentication or Verification process A user is verified as being the person s/he claims to be More suitable for establishing individuality of a person, where number of classes is too large to completely sample, eg. population of an entire nation. One-of-many decision Identification process A user is identified from within a population of n users One-of-n response Dichotomy vs. Polychotomy CS 691 - Team 5 Biometric Authentication System

  6. Original Feature Vector Data File CS 691 - Team 5 Biometric Authentication System

  7. Dichotomy Converted File CS 691 - Team 5 Biometric Authentication System

  8. Dichotomy Conversion Example • First row : • SAME , 254 , |0.11431427822210534 - 0.0|,.. • Fifth row : • DIFF, 254, |0.11431427822210534 - 0.32848686484618|,.. • Total number of • Intra (SAME) class data samples : • m * (m-1) * n /2 • Inter (DIFF) class data samples : • m * m * n * (n-1) /2 • Where • n = number of subjects • m = number samples from each subject • For the given example : • Intra-class size = 40; Inter-class size = 150; n=4, m=5 CS 691 - Team 5 Biometric Authentication System

  9. Polychotomy to Dichotomy Conversion Reference:http://www.icgst.com/gvip/v5/P1150511001.pdf CS 691 - Team 5 Biometric Authentication System

  10. System Evaluation • FRR (False Reject Rate) • Same person’s biometric data identified as coming from two different people • FAR (False Accept Rate) • Biometric data provided by two different people are classified as coming from the same person • System Performance • Biometric data correctly classified CS 691 - Team 5 Biometric Authentication System

  11. Project Specifications • Convert training and testing files of n-class feature data into files of 2-class (inter and intra-class) dichotomy-model feature data • Prepare sets of inter and intra-class data for training and testing • Implement the nearest-neighbor technique to obtain accuracy results on the data (Euclidean distance) CS 691 - Team 5 Biometric Authentication System

  12. Application Design Decisions • Allows for users to save Test Dichotomy Data both intra and inter class data sets • Allows for users to also save the Train Dichotomy Data both intra and inter class data sets • Users are able to view a log file of what action is currently being executed • Results can be saved as a .html file to easily save and distribute them • GUI is simple, clear and easy to use CS 691 - Team 5 Biometric Authentication System

  13. Application Demonstration CS 691 - Team 5 Biometric Authentication System

  14. CS 691 - Team 5 Biometric Authentication System Tutorial

  15. Experimental Results • Experiments Performed on data obtained from • Mouse Movement biometric system • Stylometry biometric system • Keystroke biometric system • Results show • Overall System Performance % • FRR (False Reject Rate) % • FAR (False Accept Rate) %

  16. Intra-Inter class Sizes FRR(%) FAR(%) Performance(%) Train Test 1455-5100 1005-3000 64.37 22.83 66.74 1005-3000 1455-5100 58.48 23.64 68.61 Mouse Movement Results Different subjects same conditions • Training set : 115 samples from 5 subjects • 30 samples each from 3 subjects, 15 samples from 1 subject, 10 samples from 1 subject • Testing set : 90 samples from other 5 subjects • 10 samples from 3 subjects, 30 samples each from 2 subjects CS 691 - Team 5 Biometric Authentication System

  17. Intra-Inter class Sizes FRR(%) FAR(%) Performance(%) Train Test 225-1000 225-1000 72.00 16.30 73.46 225-1000 225-1000 78.66 16.30 72.24 Mouse Movement ResultsUsing all subjects; train and test sets captured 3 weeks apart • Training set : 50 samples from all 5 subjects • 10 samples from each 5 subjects • Testing set : 50 samples from all 5 subjects • 10 samples from each 5 subjects ; approximately 3 week interval CS 691 - Team 5 Biometric Authentication System

  18. Intra-Inter class Sizes FRR(%) FAR(%) Performance(%) Train Test 270-1500 270-1500 91.11 10.80 76.94 270-1500 270-1500 73.70 24.86 67.68 Stylometry Results Different subjects same conditions • Training set : 60 samples from 6 subjects • 10 samples from each 6 subjects • Testing set : 60 samples from other 6 subjects • 10 samples from each 6 subjects CS 691 - Team 5 Biometric Authentication System

  19. Intra-Inter class Sizes FRR(%) FAR(%) Performance(%) Train Test 120-1650 120-1650 93.33 5.27 88.75 120-1650 120-1650 85.83 10.12 84.74 Stylometry ResultsTrain and test set on all subjects by dividing the samples • Training set : 60 samples from all 12 subjects • 5 samples from each 12 subjects • Testing set : 60 samples from all 12 subjects • 5 samples from each 12 subjects CS 691 - Team 5 Biometric Authentication System

  20. Conditions Intra-Inter ClassSizes FRR(%) FAR(%) Performance(%) Train Test Desktop/ Copy 180-3825 180-3825 11.11 6.01 93.75 Laptop/ Copy 180-3825 180-3825 7.77 4.36 95.48 Desktop/ Free 171-3570 176-3740 28.40 1.39 97.39 Laptop/ Free 180-3825 180-3825 15.55 3.73 95.73 Keystroke ResultsDifferent Subjects Same Conditions • Training set : 90 samples from 18 subjects • 5 samples from each 18 subjects • Testing set : 90 samples from other 18 subjects • 5 samples from each 18 subjects; all intra-inter data used CS 691 - Team 5 Biometric Authentication System

  21. Conditions Intra-Inter Class Sizes FRR (%) FAR (%) Performance (%) Train Test Desktop/ Copy 180-500 180-500 10.00 13.40 87.50 Laptop/ Copy 180-500 180-500 1.66 10.20 92.05 Desktop/ Free 171-500 176-500 18.75 5.00 91.42 Laptop/ Free 180-500 180-500 9.44 10.80 89.55 Keystroke ResultsDifferent Subjects Same Conditions – Using a randomized set of 500 inter-class data • Training set : 90 samples from 18 subjects • 5 samples from each 18 subjects • Testing set : 90 samples from other 18 subjects • 5 samples from each 18 subjects; 500 intra-inter sets used CS 691 - Team 5 Biometric Authentication System

  22. Conditions Intra-Inter Class Sizes FRR (%) FAR (%) Performance (%) Train Test Train Test Desktop/ Copy Desktop/ Free 360-500 347-500 8.06 17.80 86.18 Desktop/ Free Desktop/ Copy 347-500 360-500 3.33 13.00 91.04 Laptop/ Copy Laptop/ Free 360-500 360-500 3.61 40.40 75.00 Laptop/ Free Laptop/ Copy 360-500 360-500 5.83 3.40 95.58 Desktop/ Copy Laptop/ Copy 360-500 360-500 5.27 6.80 93.83 Laptop/ Copy Desktop/ Copy 360-500 360-500 4.72 18.00 87.55 Desktop/ Free Laptop/ Free 347-500 360-500 3.05 38.80 76.16 Laptop/ Free Desktop/ Free 360-500 347-500 8.93 6.80 92.32 Desktop/ Copy Laptop/ Free 360-500 360-500 5.83 22.20 84.65 Laptop/ Free Desktop/ Copy 360-500 360-500 5.27 8.79 92.67 Desktop/ Free Laptop/ Copy 347-500 360-500 1.66 14.39 90.93 Laptop/ Copy Desktop/ Free 360-500 347-500 3.17 27.60 82.40 Keystroke ResultsTest results for “old” keystroke data (180 samples : 36 subjects 5 samples each) on same subjects and different conditions. CS 691 - Team 5 Biometric Authentication System

  23. Condition Intra-Inter Class Sizes FRR (%) FAR (%) Performance (%) Train Test Desktop/ Copy 40-150 40-150 2.50 4.66 95.78 Laptop/ Copy 40-150 40-150 2.50 10.00 91.57 Desktop/ Free 40-150 40-150 0.00 4.66 96.31 Laptop/ Free 40-150 40-150 0.00 10.00 92.10 Keystroke ResultsLongitudinal authentication test results on same subjects and conditions but at two-week data collection interval. • Training set (baseline) : 20 samples from 4 subjects • 5 samples from each 4 subjects • Testing set (2-week interval): 20 samples from 4 subjects • 5 samples from each 4 subjects CS 691 - Team 5 Biometric Authentication System

  24. Condition Intra-Inter class Sizes FRR (%) FAR (%) Performance (%) Train Test Desktop/ Copy 40-150 40-150 2.50 12.66 89.47 Laptop/ Copy 40-150 40-150 0.00 0.00 100.00 Desktop/ Free 40-150 40-150 2.50 1.33 98.42 Laptop/ Free 40-150 40-150 0.00 8.00 93.68 Keystroke ResultsLongitudinal authentication test results on same subjects and conditions but at four-week data collection interval. • Training set (baseline) : 20 samples from 4 subjects • 5 samples from each 4 subjects • Testing set (4-week interval): 20 samples from 4 subjects • 5 samples from each 4 subjects CS 691 - Team 5 Biometric Authentication System

  25. Project Achievements • Utilized the dichotomy model in the authentication of biometric data obtained from the Keystroke, Stylometry and Mouse Movement biometric systems. • Sought to establish that the dichotomy model is the preferred model over the polychotomy model when dealing with an enormous number of classes where the whole population is not available for sampling, that it is the statistically inferable approach. CS 691 - Team 5 Biometric Authentication System

  26. Summary of Results • For the mouse movement and stylometry biometric data – small number of users (classes) • System performance : between 66% and 76% • FAR and FRR : high • For the keystroke biometric data - large number of users (classes) • System performance : above 90% in most cases • FAR : less than 15% in most cases • FRR : almost always less than 10%. CS 691 - Team 5 Biometric Authentication System

  27. Conclusion • The results on the keystroke biometric data are encouraging and indicate that the dichotomy model may be a feasible solution to the authentication problem when a large number of classes are involved. CS 691 - Team 5 Biometric Authentication System

  28. Future Work • Comparative analysis of the dichotomy authentication results with polychotomy authentication results obtained on the same keystroke biometric data. • Study to see whether the results for the mouse movement and stylometry data improved significantly as the sample sizes increased. CS 691 - Team 5 Biometric Authentication System

  29. Please Visit Our Website To obtain the latest downloads and information please visit us online. http://utopia.csis.pace.edu/cs691/2007-2008/team5/index.html a CS 691 - Team 5 Biometric Authentication System

  30. Thank you CS 691 - Team 5 Biometric Authentication System

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