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Mouse Movement Project

Mouse Movement Project

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Mouse Movement Project

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  1. Mouse Movement Project Customer: Larry Immohr Professor: Dr. Charles Tappert Team: Shinese Noble Anil Ramapanicker Pranav Shah Adam Weiss

  2. Agenda • Brief description of project • Project Requirements • Meetings • Design Decisions • Components • Testing Strategy • Challenges • Questions

  3. Description of Project • Creation of a pattern recognition system for Mouse Movement Biometrics • Identify computer users thru their individual mouse movements • Provides a feasibility study on whether this is a relevant way to track computer users behavior and identify them.

  4. Description of Project • Enough individuality in a users mouse movements to identify them • Arc • Speed • Acceleration / Deceleration • Clicking • Collect Data  Identify Features  Classify User

  5. Mouse Movement Biometric System Enrolment Mode Enrolment Data and User Mouse action data Data Storage csv files User Mode User Mouse action data Feature Extraction and Profile creation Feature Vector creation Identification Result Success Statistics Classifies the feature vector. Finds the nearest neighbors Mouse Movement Biometric System- High-Level View

  6. Project Requirements • Capture data of individual mouse user • Mouse Movement • Mouse Click • Store data • Perform calculations to quantify mouse movements • Utilize data to identify user

  7. Meetings • Team met via phone conference every Monday • Constant communication via e-mail • Meeting with client via phone conference every Tuesday • Communication via e-mail • Sharing documentation

  8. Design Decisions • Modular Format • Runs in Background • Can be layered with any application • Utilizes an enrollment program to get “fingerprint” of user • Focused on a limited number of features due to time and resource constraints • Additional requirements can be built in as project continues

  9. Components • 3 modules of the program: • Data Capture • Feature Extraction • Classification

  10. Data Capture Module Feature Extractor Module Data Files Feature Vector Files Classifier Module Results Success Statistics Design Decisions – 3 Programs

  11. Tic-Tac-Toe Game Button training Blank screen User Task Area Mouse Monitoring Module Data Collection Module Standalone Application Data Files Data Capture – Architectural View

  12. Data Capture - Enrollment

  13. Data Capture - Data Mouse User Action Event Time in Milliseconds X Coordinate Y Coordinate

  14. Feature Extraction • Reads the raw data file • Parses data into mouse curves and mouse clicks • Compute individual curve and click measurements • Creates a mouse profile of user • Creates mouse profile measurements

  15. MouseProfile Is a vector of curves and clicks? ProfileMeasure measure of many curves and clicks MouseClicks This is a vector of clicks. A profile contains many clicks MouseCurves This is a vector of curves. A profile contains many curves MousePoints Each curve is a vector of mouse points. CurveMeasure Each curve has one Measure object MousePoints Each click is having two mouse points. ClickMeasure Each click has one Measure object MouseData Each point is represented with MouseData: • Action • Time • x • y Each measure object can have many measures in it. • duration MouseData Each point is represented with MouseData: • Action • Time • x • y Each measure object can have many measures in it. • Speed • Length of the curve • Time of the curve • Curvature Feature Extraction View

  16. Feature Extraction - Data

  17. Classification • Takes the feature vectors as the input • Normalizes the data • Uses K-Nearest Neighbor algorithm for a test case • Does a leave one out method for cross validation between many cases • Prints out the matching cases • Analyze the cross validation results and prints out the success statistics

  18. Testing Strategy • Multiple releases • Testing amongst team for bugs • Delivered to client after team testing • Repeated for each release • For program data, all members input 5 samples of data

  19. Challenges • Establishment of clear goals • Change in scope of project • New project; many unknowns • How to utilize enrollment program

  20. Questions?