keystroke biometric : roc experiments

Keystroke Biometric: ROC Keystroke Biometric: ROC ExperimentsExperiments

Keystroke Biometric : ROC ExperimentsKeystroke Biometric : ROC Experiments

TeamTeamAbhishek KanchanAbhishek KanchanPriyanka RanadivePriyanka Ranadive

Sagar DesaiSagar DesaiPooja MalhotraPooja Malhotra

Ning WangNing Wang


WHAT IS KEYSTROKE BIOMETRIC ?WHAT IS KEYSTROKE BIOMETRIC ?

• The keystroke biometric is one of the less-studied behavioral biometrics.

• Keystroke biometric systems measure typing characteristics believed to be unique to an individual and difficult to duplicate.

• Used for Identification• Used for Authentication• Developed over the past 6+ years


Introduction to ROC CurvesIntroduction to ROC CurvesUsed for binary decisions

Signal detection – signal / no signal Medical diagnosis – disease / no disease Biometric authentication – you are the

person you claim to be / you are not


Introduction to ROC CurvesIntroduction to ROC CurvesIn biometrics the ROC curve varies from FAR=1 &

FRR=0 at one end to FAR=0 & FRR=1 at other FAR = False Accept Rate – the rate an imposter is

falsely accepted FRR = False Reject Rate – the rate the correct

person is falsely rejected

ROC Charts are expressed in terms of percentages (0-100%) or probabilities (0-1). These are used interchangeably.


ROC Authentication Analogy • Supreme Court – nine judges– Usual procedure – majority required to make decision– Like 9NN needing majority to authenticate a user

• ROC Curve – effectively creates many potential procedures and provides FAR/FRR tradeoff for each (here is the m-kNN method)– Need 9 votes to make decision (very conservative)– Need 8, 7, 6 votes to make decision (conservative)– Need 5 votes to make decision (majority)– Need 4, 3, 2 votes to make decision (liberal)– Need 1 or even 0 votes to make decision (very liberal)


ROC EXPERIMENTSROC EXPERIMENTS• Derived from four nonparametric techniques. • ‘Weak' and ‘Strong' training experiments.–Weak Enrollment data, only non-test-

subject data is used to train the system.– Strong enrollment uses test-subject data to

train the system, and then uses independent (different) test-subject data to test the system.

• Large Data Experiments


SYSTEM SYSTEM OVERVIEWOVERVIEW


Parametric Procedures Parametric

techniques are well studied.

Data follows a normal or Gaussian distribution.

Vary a threshold to obtain the tradeoff between FAR/FRR.

Probability density functions can be calculated without estimation.

Parametric ROC - Probability Density Function - Adapted from Cha, et al (2009)


Cha Dichotomy Model Simplifies

complexity Transforms a

feature space into a distance vector space.

Uses distance measures.

Multi-class to two Class Transformation Process, Adapted from Yoon et al (2005)


Pure Rank Method – m-kNN

Pure Rank Method. Evaluate the top 7

NN. Q is authenticated

if # within-class matches is >= decision threshold of 4NN.

Unweighted. All W’s are equal in weight.


Rank Method Weighted by Rank Order wm-kNN Authenticate if W

choices are > weighted match (m)

Score varies from 0 to =k(k+1)/2

For every m, FAR/FRR pair or ROC point.

If m=0, FAR=1, FAR=0 …All users accepted.

If m=15, FAR=small, FRR=large, few Q’s accepted.


m-kNN and wm-kNN ROC’s

LapFree – Weak Training


Distance Threshold Method t-kNN

A positive vote is within a distance threshold from the user’s sample.

Uses feature vector space distances only.

At 0, no distance vectors are authenticated. FAR=0, FRR=100%. At t=100, all distance vectors are authenticated. FAR=100, FRR=0.


Threshold (t-kNN) Method

DeskFree (left) and LapFree (right) Data


Threshold (ht-kNN) Method Weighted vote

based on distances to the kNN.

Hybrid of rank method and vector space distances.

For each test sample, the within-class weight (WCW) is calculated based on the distance vectors.

DeskFree (left) and LapFree (right) Data


Weak & Strong Training


DELIVERABLEDELIVERABLE• Deliverable 5 – Authentication Experiments – Ideal

Conditions/ Weak Enrollment Part IStatus – Completed

• Deliverable 6 - Authentication Experiments – Ideal Conditions/ Weak Enrollment Part II

Status – Completed

• Deliverable 7 – Enhance and Correct Refactor-BAS.jar ROC interface

Status - Completed


DELIVERABLE 7DELIVERABLE 7

• Implement Perl ROC with threshold logic in JAVA.

• Unify the code in Java which was supported by a Perl program earlier for calculating ROC threshold Values.

• Maintain the performance of Perl code in Java.• Some changes in User Interface of ROC

program.


UI CHANGESUI CHANGES


TEAM COMMUNICATIONTEAM COMMUNICATION

• Google Group for information sharing and discussion

• Skype Meetings

• Emails

• Personal Meetings

• Documented Minutes of Meeting

• Team Website status updates

• Assigned Task progress check by team leader


Questions?

keystroke biometric : roc experiments

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