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Touch-Screen Mobile-Device Data Collection for Biometrics Studies

Touch-Screen Mobile-Device Data Collection for Biometrics Studies. W. Ciaurro, B. Major, D. Martinez, D. Panchal, G. Perez, M. Rana, R. Rana, R. Reyes, S. Rodriguez, R. Valdez, D. Zuluaga. Research Purpose. Assist in Data Collection Understand Biometric Security Improve Authentication System

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Touch-Screen Mobile-Device Data Collection for Biometrics Studies

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  1. Touch-Screen Mobile-Device Data Collection for Biometrics Studies W. Ciaurro, B. Major, D. Martinez, D. Panchal, G. Perez, M. Rana, R. Rana, R. Reyes, S. Rodriguez, R. Valdez, D. Zuluaga

  2. Research Purpose • Assist in Data Collection • Understand Biometric Security • Improve Authentication System • What Features are best at Authentication? • 2008 US Higher Education Opportunity Act • Mobile Device Authentication

  3. Biometric Technology • Physical versus Behavioral characteristics • Keystrokes biometrics • Physiological biometrics • Use a person’s physical attributes • Ex. fingerprint, face or iris recognition for identification of user identity. • Behavioral biometrics • Use a person’s speech, writing, or keystrokes for suchverification.

  4. Literature Review • Long-text-input keystroke studies at Pace University. • Touchscreen input for continuous authentication at University of Oxford. • Passive user authentication at Carnegie Mellon University.

  5. Project Details • 10 college participants • 3 days of data gathering • Android phone, recorded features and gestures; • Text inputs • Number pad inputs • Other inputs (scrolling, pinching, etc.)

  6. Project Details Keyboard Recorded Features: • Duration • Transitions • Pressure Gesture Recorded Features • Locations of touch • Major/Minor Axis of touch • Touch Timing • Gyroscope Recording

  7. Hardware A variety of Android devices were used to capture data, including: • LG Nexus 5 • LG Nexus 4 • Sony Xperia Z1 Pros of Android: • Ease of Data Capture (lots of sensors) • Open Source (Java platform)

  8. Software • Android Development Environments: 1. Eclipse 2. NetBeans • Both Java IDEs • Data Collection Apps: • Biometric Soft Keyboard • Text Data Entry & Numeric Data Entry • Biometric Gestures Capture

  9. Experiments Scenario 1 - Numeric Input: The numeric data samples were collected using the "Numeric Data Entry" app which consisted in entering the numeric sequence (9141937761) followed by a enter key. Scenario 2 - Text Input: The character data samples were collected using the "Text Data Entry" app which involved entering 2 sets of characters similar to composing a large text message.

  10. Experiments continued Scenario 3 - Gestures: The touch gestures data samples were collected using the biometric gesture app. The 1st phase was to zoom in and out to locate an object within a picture. The 2nd phase was to zoom in on a question click on the answer them zoom out and go to the next questions. One of our advantages was that we had no restrictions. Since this is a behavioral study each subject was unique to how they enter their keystroke.

  11. Literature Construction • Learned the proper means of writing about research. • Developed real world experience. • Learned the challenges of working in groups on a schedule

  12. Conclusion • We were able to take part in a graduate study. • Deeper understanding of the experiment process. • Chance to develop professional research publication. • We have hopes of implementing a Biometric System in the future.

  13. Special Thanks We would like to thank Gonzalo Perez,our project manager, Andreea Cotoranu and Charles Tappert, our research advisors, and all PACE Graduate Students involved!.

  14. Thank You! Q&A

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