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# CS 691 - Team 5 - PowerPoint PPT Presentation

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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#### Presentation Transcript

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

• Report results using standard authentication system performance statistics

CS 691 - Team 5

Biometric Authentication System

### 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

### 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

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

### Original Feature Vector Data File

CS 691 - Team 5

Biometric Authentication System

### Dichotomy Converted File

CS 691 - Team 5

Biometric Authentication System

### 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

### Polychotomy to Dichotomy Conversion

Reference:http://www.icgst.com/gvip/v5/P1150511001.pdf

CS 691 - Team 5

Biometric Authentication System

### 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

### 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

### 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

### Application Demonstration

CS 691 - Team 5

Biometric Authentication System

CS 691 - Team 5

Biometric Authentication System Tutorial

### 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) %

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

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

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

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

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

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

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

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

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

### 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

### 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

### 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

### 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

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CS 691 - Team 5

Biometric Authentication System

## Thank you

CS 691 - Team 5

Biometric Authentication System