CS Team 5 Alex Wong Raheel Khan Rumeiz Hasseem Swati Bharati Biometric Authentication System

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

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) 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 Biometric Authentication System CS Team 5

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 Biometric Authentication System CS Team 5

Dichotomy vs. Polychotomy 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 Biometric Authentication System CS Team 5

Original Feature Vector Data File Biometric Authentication System CS Team 5

Dichotomy Converted File Biometric Authentication System CS Team 5

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

Polychotomy to Dichotomy Conversion Reference: Biometric Authentication System CS Team 5

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

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 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 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) Implement the nearest-neighbor technique to obtain accuracy results on the data (Euclidean distance) Biometric Authentication System CS Team 5

Application Design Decisions Allows for users to save Test Dichotomy Data both intra and inter class data sets 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 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 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 Results can be saved as a.html file to easily save and distribute them GUI is simple, clear and easy to use GUI is simple, clear and easy to use Biometric Authentication System CS Team 5

Application Demonstration Biometric Authentication System CS Team 5

Biometric Authentication System Tutorial

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

Mouse Movement Results Different subjects same conditions Intra-Inter class Sizes FRR (%)FAR (%)Performance (%) TrainTest Biometric Authentication System CS Team 5  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

Mouse Movement Results Using all subjects; train and test sets captured 3 weeks apart Intra-Inter class Sizes FRR (%)FAR (%)Performance (%) TrainTest Biometric Authentication System CS Team 5  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

Stylometry Results Different subjects same conditions Intra-Inter class Sizes FRR (%)FAR (%)Performance (%) TrainTest Biometric Authentication System CS Team 5  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

Stylometry Results Train and test set on all subjects by dividing the samples Intra-Inter class Sizes FRR (%)FAR (%)Performance (%) TrainTest Biometric Authentication System CS Team 5  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

Keystroke Results Keystroke Results Different Subjects Same Conditions Conditions Intra-Inter Class Sizes FRR (%) FAR (%)Performance (%) TrainTest Desktop/ Copy Laptop/ Copy Desktop/ Free Laptop/ Free Biometric Authentication System CS Team 5  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

Keystroke Results Keystroke Results Different Subjects Same Conditions – Using a randomized set of 500 inter-class data Conditions Intra-Inter Class Sizes FRR (%) FAR (%) Performance (%) TrainTest Desktop/ Copy Laptop/ Copy Desktop/ Free Laptop/ Free Biometric Authentication System CS Team 5  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

Keystroke Results Keystroke Results Test results for “old” keystroke data (180 samples : 36 subjects 5 samples each) on same subjects and different conditions. ConditionsIntra-Inter Class Sizes FRR (%) FAR (%)Performance (%) TrainTestTrainTest Desktop/ Copy Desktop/ Free Desktop/ Free Desktop/ Copy Laptop/ Copy Laptop/ Free Laptop/ Free Laptop/ Copy Desktop/ Copy Laptop/ Copy Laptop/ Copy Desktop/ Copy Desktop/ Free Laptop/ Free Laptop/ Free Desktop/ Free Desktop/ Copy Laptop/ Free Laptop/ Free Desktop/ Copy Desktop/ Free Laptop/ Copy Laptop/ Copy Desktop/ Free Biometric Authentication System CS Team 5

Keystroke Results Keystroke Results Longitudinal authentication test results on same subjects and conditions but at two-week data collection interval. Condition Intra-Inter Class Sizes FRR (%)FAR (%)Performance (%) TrainTest Desktop/ Copy Laptop/ Copy Desktop/ Free Laptop/ Free Biometric Authentication System CS Team 5  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

Keystroke Results Keystroke Results Longitudinal authentication test results on same subjects and conditions but at four-week data collection interval. Condition Intra-Inter class Sizes FRR (%)FAR (%)Performance (%) TrainTest Desktop/ Copy Laptop/ Copy Desktop/ Free Laptop/ Free Biometric Authentication System CS Team 5  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

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. Biometric Authentication System CS Team 5

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

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. 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. Biometric Authentication System CS Team 5

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

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