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

Fitt’s 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

significant (p < 0.05).The relationship between MT-B and ID-B is statistically

> 0.50.10.050

NoYes

P = 0.000

by the regression model.45.12% of the variation in MT-B can be accounted for

100%0%

R-sq (adj) = 45.12%

ID-B increases, MT-B also tends to increase.The positive correlation (r = 0.68) indicates that when

10-1

0.68

3.02.52.01.51.0

1500

1000

500

ID-B

MT-B

causes Y.A statistically significant relationship does not imply that X for MT-B.ID-B that correspond to a desired value or range of valuesto predict MT-B for a value of ID-B, or find the settings forIf the model fits the data well, this equation can be used Y = 500.5 + 286.6 Xrelationship between Y and X is:The fitted equation for the linear model that describes the

Y: MT-BX: ID-B

Is there a relationship between Y and X?

Fitted Line Plot for Linear ModelY = 500.5 + 286.6 X

Comments

Regression for MT-B vs ID-BSummary Report

% of variation accounted for by model

Correlation between Y and XNegative No correlation Positive

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

statistically significant (p > 0.05).The relationship between MT-0A and ID-0A is not

> 0.50.10.050

NoYes

P = 0.955

by the regression model.0.00% of the variation in MT-0A can be accounted for

100%0%

R-sq (adj) = 0.00%

statistically significant (p > 0.05).The correlation between MT-0A and ID-0A is not

10-1

0.00

1.61.20.80.40.0

4500

3000

1500

0

ID-0A

MT-0

A

causes Y.A statistically significant relationship does not imply that X values for MT-0A.for ID-0A that correspond to a desired value or range ofto predict MT-0A for a value of ID-0A, or find the settingsIf the model fits the data well, this equation can be used Y = 56.98 - 4.17 Xrelationship between Y and X is:The fitted equation for the linear model that describes the

Y: MT-0AX: ID-0A

Is there a relationship between Y and X?

Fitted Line Plot for Linear ModelY = 56.98 - 4.17 X

Comments

Regression for MT-0A vs ID-0ASummary Report

% of variation accounted for by model

Correlation between Y and XNegative No correlation Positive

statistically significant (p < 0.05).The relationship between MT-0Bc and ID-0Bc is

> 0.50.10.050

NoYes

P = 0.000

by the regression model.2.06% of the variation in MT-0Bc can be accounted for

100%0%

R-sq (adj) = 2.06%

ID-0Bc increases, MT-0Bc also tends to increase.The positive correlation (r = 0.15) indicates that when

10-1

0.15

0.80.60.40.20.0

3000

2000

1000

0

ID-0Bc

MT-0

Bc

causes Y.A statistically significant relationship does not imply that X range of values for MT-0Bc.settings for ID-0Bc that correspond to a desired value orto predict MT-0Bc for a value of ID-0Bc, or find theIf the model fits the data well, this equation can be used Y = 29.77 + 460.4 Xrelationship between Y and X is:The fitted equation for the linear model that describes the

Y: MT-0BcX: ID-0Bc

Is there a relationship between Y and X?

Fitted Line Plot for Linear ModelY = 29.77 + 460.4 X

Comments

Regression for MT-0Bc vs ID-0BcSummary Report

% of variation accounted for by model

Correlation between Y and XNegative No correlation Positive

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 & IDR-sq (adj) denotes how much variation can be accounted for in the linear modelCorrelation coefficient – indicates correlation strength and direction

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.

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: 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

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