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Mouse Movement Mouse Movement BiometricsBiometrics

Fall 2007 Capstone -Team MembersFall 2007 Capstone -Team MembersRafael DiazRafael Diaz

Michael LampeMichael LampeNkem AjuforNkem Ajufor

Mohammed IslamMohammed IslamAntony AmalrajAntony Amalraj

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Mouse Movement Biometrics -Mouse Movement Biometrics -Agenda of Final PresentationAgenda of Final Presentation• Brief Scope of the project Brief Scope of the project • Project Requirements and SpecificationProject Requirements and Specification• Design Decisions Design Decisions • ObjectivesObjectives• Demonstration of MMSystemDemonstration of MMSystem• Testing StrategyTesting Strategy• Meetings FormatMeetings Format• ChallengesChallenges• Wrap up/Summary of Wrap up/Summary of AccomplishmentsAccomplishments • RecommendationsRecommendations• QuestionsQuestions

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Mouse Movement Mouse Movement Biometrics BriefBiometrics Brief Scope of Scope of the projectthe project

• This semester's project had two primary This semester's project had two primary focuses. focuses.

– First, we became familiar with the system First, we became familiar with the system and collected as much additional data as and collected as much additional data as possible, including data from each team possible, including data from each team member and third party data member and third party data

– Second, and most importantly, we formatted Second, and most importantly, we formatted the feature-vector data for ease of the feature-vector data for ease of processing by other back-end teams, by processing by other back-end teams, by normalizing feature-vector data. normalizing feature-vector data.

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Mouse Movement Biometrics Mouse Movement Biometrics Project RequirementsProject Requirements• Capture data of individual mouse user (a total of 50 data files)Capture data of individual mouse user (a total of 50 data files)

– Mouse MovementMouse Movement– Mouse ClickMouse Click

• Generate corresponding feature data in normalized feature Generate corresponding feature data in normalized feature format for the backend teamsformat for the backend teams

• Perform calculations to quantify mouse movementsPerform calculations to quantify mouse movements

• Obtain recognition accuracy (just first-choice nearest Obtain recognition accuracy (just first-choice nearest neighbor) using the leave-one-out procedure on the 50 data neighbor) using the leave-one-out procedure on the 50 data files.files.

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Mouse Movement as a Mouse Movement as a Biometric Measurement - Biometric Measurement - SpecificationsSpecifications• Mouse Movement data was captured through the enrollment process Mouse Movement data was captured through the enrollment process

and the creation of a user profileand the creation of a user profile• The intent of data capturing is to identify the user based on the stored The intent of data capturing is to identify the user based on the stored

data and the data that was recently captureddata and the data that was recently captured• This method of identification, in which the data recently captured is This method of identification, in which the data recently captured is

compared to the information on the database, is known as a One to compared to the information on the database, is known as a One to Many comparisonMany comparison

• Throughout this phase of the project the data captured and used was Throughout this phase of the project the data captured and used was validated with both the K- Nearest Neighbor and validated with both the K- Nearest Neighbor and Leave One Out Leave One Out methodsmethods

• Below are some of the focus points in this Mouse Movement Study:Below are some of the focus points in this Mouse Movement Study:

– Obtain data while user clicking buttons or enrolling user infoObtain data while user clicking buttons or enrolling user info– Capture the data in a CSV format for normalization purposesCapture the data in a CSV format for normalization purposes– Generate feature extraction data from feature extractor module.Generate feature extraction data from feature extractor module.– Classify user and possible identification using classifierClassify user and possible identification using classifier– Send a set of normalized data to backend teams and Generate Send a set of normalized data to backend teams and Generate success statistics success statistics

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Mouse Movement Biometrics – Mouse Movement Biometrics – Design decisionsDesign decisions

Mouse Movement Biometric System

Data and User Mouse action

data, GUI changes

Data Storagecsv files

Feature Vector Extraction and Profile creationNormalization

Classifies the feature vector.

Finds the nearest

neighbors

User Mouse action data

Enrollment Mode

Identification ResultSuccess Statistics

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Mouse Movement Biometrics – Mouse Movement Biometrics – Objectives Objectives   

• We reported a total of 205 data files - including We reported a total of 205 data files - including the data generated by 3the data generated by 3rdrd parties parties

• Generated normalized feature vector data files Generated normalized feature vector data files and passed it on to the backend teams (Team 5 and passed it on to the backend teams (Team 5 and 6)and 6)

• Obtained recognition accuracy (first-choice Obtained recognition accuracy (first-choice nearest neighbor – 80%) using the leave-one-out nearest neighbor – 80%) using the leave-one-out procedure using 35 data files.procedure using 35 data files.

• Obtained results from KNN method using Obtained results from KNN method using Classifier Module.Classifier Module.

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Mouse Movement Biometrics - Mouse Movement Biometrics - Objectives cont’dObjectives cont’d• Generated Data at weekly intervals - 205 files total, Generated Data at weekly intervals - 205 files total,

including 3including 3rdrd party data party data– Data from more subjectsData from more subjects– Data from random button sequencesData from random button sequences

• Enhanced mmsystem module has been developed with rich Enhanced mmsystem module has been developed with rich GUI features for the future users. GUI features for the future users.

• It also will generate an additional file called profile.txt along It also will generate an additional file called profile.txt along with Raw data files. with Raw data files. – This Profile.txt file will be used as an input for both This Profile.txt file will be used as an input for both

feature extraction and classifier module.feature extraction and classifier module.

• The team created a website to ensure all our documents, The team created a website to ensure all our documents, course software will be uploaded in a centralized location.course software will be uploaded in a centralized location.

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Mouse Movement Biometrics - Mouse Movement Biometrics - Objectives cont’dObjectives cont’d

• Enhancement of the existing front-Enhancement of the existing front-end registration process that end registration process that captures pertinent information captures pertinent information regarding the userregarding the user– User Name User Name – Output File NameOutput File Name– Gender >> Male or FemaleGender >> Male or Female– AgeAge– Right- handed or Left- handedRight- handed or Left- handed– Type of MouseType of Mouse

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Mouse Movement BiometricsMouse Movement Biometrics - - User Input GUIUser Input GUI

Input Dialog Box #1

Enter User Name

Input Dialog Box #2

Enter File Name

Input Dialog Box #3

Select your Gender

( Male / Female )

Input Dialog Box #4

Select your age

( 18-50 or N/A )

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Mouse Movement Biometrics -Mouse Movement Biometrics - User Input: User Input: ContinuedContinued

Input Dialog Box #5

Select your hand used

( Right-handed / Left Handed )

Input Dialog Box #6

Select type of mouse

( Optical Mouse / Serial Mouse

USB Mouse / Wireless Mouse )

Input Dialog Box #7

Select type of Test Screen

( Fixed 25 button sequence,

Tic-Tac-Toe Game, or

Blank Screen )

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Mouse Movement Biometrics - User Input GUI: Mouse Movement Biometrics - User Input GUI: to be continuedto be continued

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Mouse Movement Biometrics-Normalized Feature Mouse Movement Biometrics-Normalized Feature Vector ReportVector Report

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Mouse Movement Biometrics – Mouse Movement Biometrics – Demonstration of Enhanced Demonstration of Enhanced SystemSystem• Demonstration of the enhanced Demonstration of the enhanced

mouse movement (old mmsystem mouse movement (old mmsystem and new mmsystem) provide and new mmsystem) provide recommendationsrecommendations

• Overview of the Technical paperOverview of the Technical paper

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Mouse Movement Biometrics Mouse Movement Biometrics Testing strategyTesting strategy• Validation of the new code introduced to correct Validation of the new code introduced to correct

and address any bugs identified in the testing and address any bugs identified in the testing windowwindow

• Corrections to program/bugs done by team Corrections to program/bugs done by team members after response/comments received from members after response/comments received from team and volunteers that ran the applicationteam and volunteers that ran the application

• For program data, all members input 5 samples of For program data, all members input 5 samples of data and data was validated through the classifier data and data was validated through the classifier programprogram

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Mouse Movement Biometrics - Mouse Movement Biometrics - Meeting FormatMeeting Format

• Team 1 met twice a week via a conference Team 1 met twice a week via a conference bridge – Tuesdays and Fridaysbridge – Tuesdays and Fridays– Tuesday’s meeting was focused on the team and Tuesday’s meeting was focused on the team and

the overall status of the projectthe overall status of the project– Friday’s meeting was focused on questions that Friday’s meeting was focused on questions that

were presented to the clientwere presented to the client

• All conference calls lasted 1 hour in durationAll conference calls lasted 1 hour in duration

• Communication via e-mail was also used and Communication via e-mail was also used and all involved parties were copied on the e-all involved parties were copied on the e-mails.mails.

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Mouse Movement Biometrics – Wrap Mouse Movement Biometrics – Wrap up/Summary of up/Summary of Accomplishments Accomplishments

Captured raw data in a CSV format for normalization and Captured raw data in a CSV format for normalization and experiments experiments

Generated Feature vector extraction data and Normalized Feature Generated Feature vector extraction data and Normalized Feature VectorVector

Generated Data in Mushroom data format for data mining projectGenerated Data in Mushroom data format for data mining project Classified the users by KNN method and Leave One Out methodClassified the users by KNN method and Leave One Out method Generated Classified output data and Success statistics ReportGenerated Classified output data and Success statistics Report Enhanced software modules to incorporate the GUI changesEnhanced software modules to incorporate the GUI changes Generated the data in the required format Generated the data in the required format Created Mouse Movement Biometrics Technical PaperCreated Mouse Movement Biometrics Technical Paper Created a User Manual to document use of the software Created a User Manual to document use of the software Created website to store the current application modules, tested Created website to store the current application modules, tested

resultsresults Created training videos for the three applications in order to assist Created training videos for the three applications in order to assist

users in learning the system.users in learning the system. Uploaded the Technical Paper, User Manual as well as the mid term Uploaded the Technical Paper, User Manual as well as the mid term

and final presentations on the websiteand final presentations on the website

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Mouse Movement Biometrics – Mouse Movement Biometrics – ChallengesChallenges

• During the initial enrollment process During the initial enrollment process questions surrounding the application and questions surrounding the application and how to access and run the application existedhow to access and run the application existed

• Difficulties in understanding the normalization Difficulties in understanding the normalization process and using only two values (0 and 1)process and using only two values (0 and 1)

• Getting the enhancements made for the Getting the enhancements made for the existing MMSystems GUI to work in a single existing MMSystems GUI to work in a single display window display window

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Mouse Movement Biometrics - Mouse Movement Biometrics - RecommendationsRecommendations• Further enhancements to the data Capture module.Further enhancements to the data Capture module.

– Work was started on adding new data fields to the Feature Work was started on adding new data fields to the Feature Extraction and Classifier modules but will need to be Extraction and Classifier modules but will need to be continued by succeeding teams.continued by succeeding teams.

• While 100% accuracy is not probable, it seems more While 100% accuracy is not probable, it seems more experiments need to be performed to see if there is a more experiments need to be performed to see if there is a more consistent accuracy rate over time and from more generated consistent accuracy rate over time and from more generated data.data.

• Subsequent teams should focus on developing the Data Capture Subsequent teams should focus on developing the Data Capture GUI to randomize the buttons to provide more varied dataGUI to randomize the buttons to provide more varied data

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Mouse Movement Biometrics – Mouse Movement Biometrics – Recommendations cont’dRecommendations cont’d

• Subsequent teams can add more user characteristics to Subsequent teams can add more user characteristics to classify the user classify the user

• Also, Subsequent teams can add more characteristics of the Also, Subsequent teams can add more characteristics of the mouse such as right click or track wheel usemouse such as right click or track wheel use

• Finally, it would be optimal if the system was developed to Finally, it would be optimal if the system was developed to be used online with a database backend.be used online with a database backend.– This would allow for more data to be generated from a This would allow for more data to be generated from a

larger pool of users for further analysis and research .larger pool of users for further analysis and research .

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Questions/Questions/CommentsComments

• http://utopia.csis.pace.edu/cs691/200http://utopia.csis.pace.edu/cs691/2007-2008/team1/default.htm7-2008/team1/default.htm