1. Design of the Data Input Structure for a Mouse Movement Biometric System to Authenticate the Identity of Online Test Takers Handling artificial acceleration mouse movement biometrics
Fall, 2014: Frank Buckley, Vito Barnes, Thomas Corum, Stephen Gelardi, Keith Rainsford
Spring 2015: Shawn C. Gross

2. Introduction Objective Results
Design a Biometric System to Verify Test Takers on Mouse/Keystroke Input
Map Mouse Movement Trajectories for Structured and Unstructured Quizzes
Apply new insight to previous work
Does Fitts’ law apply to mouse movement trajectories?
Results
Results show that application of Fitts’ law to trajectories of either type Quiz is inconclusive.

3. Fitts’ Law
Derived from 1954 study done by Paul Fitts at Ohio State University.
Fitts’ Law is a model of human behavior derived from Shannon’s communications theory.
It models the human nervous system as communication channels, in which information is transmitted by carrying out a movement task.
The formula for Fitts’ Law:
MT =a+b * ID
MT = Movement Time
ID = Index of Difficulty
a = Y-intercept for regression line
b = coefficient for regression line
ID can be expressed several ways, where D = Distance to Target and W = Width of Target
Fitts’ original formulation: ID = log2(2D/W)
Welford’s formulation: ID=log2(D/W +1/2)
Shannon’s formulation: ID=log2(D/W +1)
Shannon’s Formula was chosen for analysis as it always produces a positive ID.

4. Procedure/Methodology
Raw mouse movement data was parsed and sorted into usable chunks
Operational Definitions established for each data field and formula
Calculations for Movement Time(MT), Length of Trajectory, Index of Difficulty(ID), Velocity, Acceleration, Slope, Direction Angle and Change in Slope were completed in Excel
Establish baseline by comparing data to Fitts’ Law Test webpage data output.
MT and ID were used in Linear Regression Analysis in Minitab
Shannon’s Formula Used to determine ID:
ID = log2 (D/W + 1)
MT = a + b * ID
Key assumptions
User’s used the same equipment (mouse & PC) for all quizzes

5. Procedure / Methodology – Fitts’ Test
Fitts’ Law Test on Berkley Website – Click on the green line. This is repeated about 45 times, with targets changing in both size and distance.
Figure on left will have a lower ID as the target is both wider and closer than the one on the right.

6. Procedure / Methodology – Fitts’ Test
Results from Fitts’ Law Tests were copied in to Excel.
Regression analysis was then performed using Minitab.

7. Procedure/Methodology
Fitts’ Law w/Shannon’s Formula for ID is MT = a + b log2 (D/W + 1) , where:
MT = Movement Time of task (Duration in milliseconds)
a = y intercept (determined through linear regression)
b = slope (determined through linear regression)
D = Distance (Length of Trajectory) calculated as
W = targetwidth provided in mouse movement data

8. Key Findings – Regression Analysis – Fitts’ Law Test
Regression in Minitab provides analysis of the statistical relationship between MT & ID (p-Value), the Linear Model fitted equation and line plot, R-sq (adj), and correlation between MT & ID

9. Key Findings – Regression Analysis Quiz Mouse Movement
Unstructured
Structured
Regression in Minitab provides analysis of the statistical relationship between MT & ID (p-Value), the Linear Model fitted equation and line plot, R-sq (adj), and correlation between MT & ID

10. Key Findings – Quiz type comparisons
Comparison of Fitts’ Test results to both Structured and Unstructured Quiz types
p-Value <= 0.05 denotes a statistical relationship between between MT & ID
R-sq (adj) denotes how much variation can be accounted for in the linear model
Correlation coefficient – indicates correlation strength and direction

11. Conclusion
Trajectories from Fitts’ Law test website did show strong statistical relationship and a reasonably well fitting linear regression line.
Trajectories studied do not follow Fitts’ law, in most cases, however more analysis is needed.
Fitts’ law is based on a one-dimensional task, whereas mouse movement is a two-dimensional task with two-dimensional targets.
It seems that as a student takes a quiz they are more likely to have errant or erratic mouse movements than those found in a Fitts’ Test. This makes sense as student taking a quiz will be checking study materials before answering or even changing answers midstream. More “thinking” on the part of the student will cause movement to deter from going directly to answer, whereas the Fitts’ Law Test does not require thinking so much as reaction to go directly to the target.

12. Why?
The data collected is the pointer motion, not the mouse motion

13. Project Description Objective Results
Investigate the problem of artificial acceleration in relation to the mouse movement biometric system
Analyze how the Windows and Mac OS X operating systems implement artificial acceleration
Reverse-engineer the artificial acceleration and implement a method to compensate mouse pointer velocity
Results
Simulator test results show how artificial acceleration can be accounted for while recording mouse movements

14. Artificial Acceleration
Created by Microsoft for the Windows XP operating system to compensate for sluggish mouse pointer movements
Artificial acceleration increases the physical velocity of the mouse cursor
Physical velocity of the mouse is the key determiner when artificial acceleration is applied
Within Mac OS X:
macScalingValue: This value resides in the system defaults. When the mouse passes this value, the velocity of the cursor increase by a factor of 2
Within Windows Registry:
mouseThreshold1: When the movement of the mouse passes this value, the speed of the cursor will increase by a factor of 2 while the velocity of the mouse continues at this rate
mouseThreshold2: Increases mouse cursor speed by a factor of 4 when the mouse movement velocity increases to this value

15. Artificial Acceleration
Formulas used to convert virtual mouse and cursor movements into physical movement speeds
Threshold Registry
Locations:

16. Artificial Acceleration - Disabling
Disabling is optimal!
Disabling artificial acceleration limits complication in user recognition
Disable in Windows:
Un-check enhance pointer precision
Disable in Mac OS X:
Enter this command into the Terminal Application
Enabled by default in:
Windows
Mac OS X
Most versions of Linux

17. Procedure/Methodology - Simulator
In order to analyze artificial acceleration, more user/system data was needed:
Screen resolution
Monitor size in inches
Threshold or scaling value
Screen DPI (Dots Per Inch)
Creation of simulation environment for testing
Records change in mouse coordinates, time, Monitor size, and Screen DPI value
Developed in Python
Includes methods to:
Calculate DPI
Physical velocity of mouse
Physical velocity of pointer
Modifier for Artificial Acceleration (Mac & Windows)
Test two separate user sessions
Artificial Acceleration enabled
Disabled

18. Procedure / Methodology – Simulator
Above is the user prompt, directing the user for a
unique user number and monitor size in inches
To the left, the sample output while the simulator
Records the velocity and outputs this data to a
CSV file

19. Procedure / Methodology – Simulator
Simulation Environment – Tracks mouse coordinates on the grey plane, recording the events into a CSV file while outputting the results in the command prompt or Terminal window

20. Procedure / Methodology – Simulator
A plot is then generated upon exit of the simulation environment with each dot indicating a recorded change in cursor location, limited by the mouse bus speed.
To the left – Results from a test session with Artificial Acceleration disabled.

21. Procedure / Methodology – Simulator
The X and Y axis reflect the upper and lower limits(of the monitor) traveled by the cursor given some additional padding for presentation purposes
To the left – Results from a test session with Artificial Acceleration enabled.

22. Procedure/Methodology - Results
Artificial Acceleration disabled
Leads to optimal results
No further work needed for user recognition
Artificial Acceleration enabled
Lower threshold(mouseThreshold1) reached
Upper threshold never reached
Longer lines between points in plot show possible limitations:
Software used in implementation
Mouse data packet generation too slow

23. Conclusion
If optimal results are desired, Artificial Acceleration should be disabled
The research in this paper however, lets us calculate the severity of the acceleration as well as the true movements of the mouse and pointer
The correct information must be queried from the user system
This work can further be applied and integrated into the current mouse movement biometric studies at Pace University
Suggestions for improving user data collection:
Query user system for:
Monitor Size (inches)
Screen Resolution
Artificial Acceleration values

24. Thank you