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
Introduction

Objective
 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.
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.
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
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.
Procedure / Methodology – Fitts’ Test
Results from Fitts’ Law Tests
were copied in to Excel.
Regression analysis was
then performed using
Minitab.
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
Key Findings – Regression Analysis – Fitts’ Law Test
Regression for MT-B vs ID-B
Summary Report
Y: MT-B
X: ID-B
Fitted Line Plot for Linear Model
Y = 500.5 + 286.6 X
Is there a relationship between Y and X?
0
0.05 0.1
> 0.5
Yes
No
% of variation accounted for by model
0%
100%
R-sq (adj) = 45.12%
45.12% of the variation in MT-B can be accounted for
by the regression model.
Negative
-1
Correlation between Y and X
No correlation
1
0.68
The positive correlation (r = 0.68) indicates that when
ID-B increases, MT-B also tends to increase.
1000
500
1.0
1.5
2.0
ID-B
2.5
3.0
Comments
Positive
0
1500
MT-B
P = 0.000
The relationship between MT-B and ID-B is statistically
significant (p < 0.05).
The fitted equation for the linear model that describes the
relationship between Y and X is:
Y = 500.5 + 286.6 X
If the model fits the data well, this equation can be used
to predict MT-B for a value of ID-B, or find the settings for
ID-B that correspond to a desired value or range of values
for MT-B.
A statistically significant relationship does not imply that X
causes Y.
Fitt’s Law Test
Regression in Minitab provides analysis of the statistical relationship between MT & ID (pValue), the Linear Model fitted equation and line plot, R-sq (adj), and correlation between
MT & ID
Key Findings – Regression Analysis Quiz Mouse Movement
Regression for MT-0A vs ID-0A
Summary Report
Y: MT-0A
X: ID-0A
> 0.5
Yes
0
4500
No
MT-0A
0.05 0.1
> 0.5
Yes
P = 0.955
The relationship between MT-0A and ID-0A is not
statistically significant (p > 0.05).
Fitted Line Plot for Linear Model
Y = 29.77 + 460.4 X
Is there a relationship between Y and X?
P = 0.000
The relationship between MT-0Bc and ID-0Bc is
statistically significant (p < 0.05).
3000
1500
% of variation accounted for by model
0%
R-sq (adj) = 0.00%
0.00% of the variation in MT-0A can be accounted for
by the regression model.
Negative
-1
Correlation between Y and X
No correlation
1
0.00
The correlation between MT-0A and ID-0A is not
statistically significant (p > 0.05).
0.0
0.4
0.8
ID-0A
1.2
Comments
Positive
0
% of variation accounted for by model
0
100%
The fitted equation for the linear model that describes the
relationship between Y and X is:
Y = 56.98 - 4.17 X
If the model fits the data well, this equation can be used
to predict MT-0A for a value of ID-0A, or find the settings
for ID-0A that correspond to a desired value or range of
values for MT-0A.
A statistically significant relationship does not imply that X
causes Y.
Unstructured
1.6
3000
No
MT-0Bc
0.05 0.1
Y: MT-0Bc
X: ID-0Bc
Fitted Line Plot for Linear Model
Y = 56.98 - 4.17 X
Is there a relationship between Y and X?
0
Regression for MT-0Bc vs ID-0Bc
Summary Report
0%
-1
Correlation between Y and X
No correlation
1
0.15
The positive correlation (r = 0.15) indicates that when
ID-0Bc increases, MT-0Bc also tends to increase.
0.0
0.2
0.4
ID-0Bc
0.6
0.8
Comments
Positive
0
1000
0
100%
R-sq (adj) = 2.06%
2.06% of the variation in MT-0Bc can be accounted for
by the regression model.
Negative
2000
The fitted equation for the linear model that describes the
relationship between Y and X is:
Y = 29.77 + 460.4 X
If the model fits the data well, this equation can be used
to predict MT-0Bc for a value of ID-0Bc, or find the
settings for ID-0Bc that correspond to a desired value or
range of values for MT-0Bc.
A statistically significant relationship does not imply that X
causes Y.
Structured
Regression in Minitab provides analysis of the statistical relationship between MT & ID (pValue), the Linear Model fitted equation and line plot, R-sq (adj), and correlation between
MT & ID
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
Conclusion

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

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.
Why?

The data collected is the pointer motion, not the
mouse motion
Project Description

Objective
 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
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
Artificial Acceleration


Formulas used to convert virtual mouse and cursor
movements into physical movement speeds
Threshold Registry
Locations:
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
Procedure/Methodology - Simulator

In order to analyze artificial acceleration, more user/system data was needed:

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Creation of simulation environment for testing



Records change in mouse coordinates, time, Monitor size, and Screen DPI value
Developed in Python
Includes methods to:

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Screen resolution
Monitor size in inches
Threshold or scaling value
Screen DPI (Dots Per Inch)
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
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
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
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.
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.
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
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
Thank you