1 / 23

Keystroke Biometric : ROC Experiments

Keystroke Biometric : ROC Experiments. Team Abhishek Kanchan Priyanka Ranadive Sagar Desai Pooja Malhotra Ning Wang. WHAT IS KEYSTROKE BIOMETRIC ?. The keystroke biometric is one of the less-studied behavioral biometrics.

devika
Download Presentation

Keystroke Biometric : ROC Experiments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Keystroke Biometric : ROC Experiments Team AbhishekKanchan Priyanka Ranadive Sagar Desai PoojaMalhotra Ning Wang

  2. 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

  3. Introduction to ROC Curves • Used 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

  4. Introduction to ROC Curves • In 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.

  5. 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)

  6. ROC 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

  7. SYSTEM OVERVIEW

  8. 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)

  9. 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)

  10. 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.

  11. 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.

  12. m-kNN and wm-kNN ROC’s LapFree – Weak Training

  13. 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.

  14. Threshold (t-kNN) Method DeskFree (left) and LapFree (right) Data

  15. Threshold (ht-kNN) Method DeskFree (left) and LapFree (right) Data • 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.

  16. Weak & Strong Training

  17. DELIVERABLE Deliverable 5 – Authentication Experiments – Ideal Conditions/ Weak Enrollment Part I Status – 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

  18. DELIVERABLE 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.

  19. UI CHANGES

  20. TEAM 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

  21. Questions?

More Related