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Mouse Movement Biometrics

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Mouse Movement Biometrics

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  1. Mouse Movement Biometrics Fall 2007 Capstone -Team Members Rafael Diaz Michael Lampe Nkem Ajufor Mohammed Islam Antony Amalraj Mouse Movement Biometrics, Pace University, Fall'2007

  2. Mouse Movement Biometrics -Agenda of Final Presentation • Brief Scope of the project • Project Requirements and Specification • Design Decisions • Objectives • Demonstration of MMSystem • Testing Strategy • Meetings Format • Challenges • Wrap up/Summary of Accomplishments • Recommendations • Questions Mouse Movement Biometrics, Pace University, Fall'2007

  3. Mouse Movement Biometrics BriefScope of the project • This semester's project had two primary focuses. • First, we became familiar with the system and collected as much additional data as possible, including data from each team member and third party data • Second, and most importantly, we formatted the feature-vector data for ease of processing by other back-end teams, by normalizing feature-vector data. Mouse Movement Biometrics, Pace University, Fall'2007

  4. Mouse Movement Biometrics Project Requirements • Capture data of individual mouse user (a total of 50 data files) • Mouse Movement • Mouse Click • Generate corresponding feature data in normalized feature format for the backend teams • Perform calculations to quantify mouse movements • Obtain recognition accuracy (just first-choice nearest neighbor) using the leave-one-out procedure on the 50 data files. Mouse Movement Biometrics, Pace University, Fall'2007

  5. Mouse Movement as a Biometric Measurement - Specifications Mouse Movement data was captured through the enrollment process and the creation of a user profile The intent of data capturing is to identify the user based on the stored data and the data that was recently captured This method of identification, in which the data recently captured is compared to the information on the database, is known as a One to Many comparison Throughout this phase of the project the data captured and used was validated with both the K- Nearest Neighbor and Leave One Out methods Below are some of the focus points in this Mouse Movement Study: Obtain data while user clicking buttons or enrolling user info Capture the data in a CSV format for normalization purposes Generate feature extraction data from feature extractor module. Classify user and possible identification using classifier Send a set of normalized data to backend teams and Generate success statistics Mouse Movement Biometrics, Pace University, Fall'2007

  6. Mouse Movement Biometric System Enrollment Mode Data and User Mouse action data, GUI changes Data Storage csv files User Mouse action data Feature Vector Extraction and Profile creation Normalization Identification Result Success Statistics Classifies the feature vector. Finds the nearest neighbors Mouse Movement Biometrics – Design decisions Mouse Movement Biometrics, Pace University, Fall'2007

  7. Mouse Movement Biometrics – Objectives We reported a total of 205 data files - including the data generated by 3rd parties Generated normalized feature vector data files and passed it on to the backend teams (Team 5 and 6) Obtained recognition accuracy (first-choice nearest neighbor – 80%) using the leave-one-out procedure using 35 data files. Obtained results from KNN method using Classifier Module. Mouse Movement Biometrics, Pace University, Fall'2007

  8. Mouse Movement Biometrics - Objectives cont’d • Generated Data at weekly intervals - 205 files total, including 3rd party data • Data from more subjects • Data from random button sequences • Enhanced mmsystem module has been developed with rich GUI features for the future users. • It also will generate an additional file called profile.txt along with Raw data files. • This Profile.txt file will be used as an input for both feature extraction and classifier module. • The team created a website to ensure all our documents, course software will be uploaded in a centralized location. Mouse Movement Biometrics, Pace University, Fall'2007

  9. Mouse Movement Biometrics - Objectives cont’d • Enhancement of the existing front-end registration process that captures pertinent information regarding the user • User Name • Output File Name • Gender >> Male or Female • Age • Right- handed or Left- handed • Type of Mouse Mouse Movement Biometrics, Pace University, Fall'2007

  10. Mouse Movement Biometrics - User Input GUI Input Dialog Box #1 Enter User Name Input Dialog Box #4 Select your age ( 18-50 or N/A ) Input Dialog Box #2 Enter File Name Input Dialog Box #3 Select your Gender ( Male / Female ) Mouse Movement Biometrics, Pace University, Fall'2007

  11. Mouse Movement Biometrics -User Input: Continued 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 ) Mouse Movement Biometrics, Pace University, Fall'2007

  12. Mouse Movement Biometrics - User Input GUI: to be continued Mouse Movement Biometrics, Pace University, Fall'2007

  13. Mouse Movement Biometrics-Normalized Feature Vector Report Mouse Movement Biometrics, Pace University, Fall'2007

  14. Mouse Movement Biometrics – Demonstration of Enhanced System • Demonstration of the enhanced mouse movement (old mmsystem and new mmsystem) provide recommendations • Overview of the Technical paper Mouse Movement Biometrics, Pace University, Fall'2007

  15. Mouse Movement Biometrics, Pace University, Fall'2007

  16. Mouse Movement Biometrics Testing strategy Validation of the new code introduced to correct and address any bugs identified in the testing window Corrections to program/bugs done by team members after response/comments received from team and volunteers that ran the application For program data, all members input 5 samples of data and data was validated through the classifier program Mouse Movement Biometrics, Pace University, Fall'2007

  17. Mouse Movement Biometrics - Meeting Format Team 1 met twice a week via a conference bridge – Tuesdays and Fridays Tuesday’s meeting was focused on the team and the overall status of the project Friday’s meeting was focused on questions that were presented to the client All conference calls lasted 1 hour in duration Communication via e-mail was also used and all involved parties were copied on the e-mails. Mouse Movement Biometrics, Pace University, Fall'2007

  18. Mouse Movement Biometrics – Wrap up/Summary of Accomplishments • Captured raw data in a CSV format for normalization and experiments • Generated Feature vector extraction data and Normalized Feature Vector • Generated Data in Mushroom data format for data mining project • Classified the users by KNN method and Leave One Out method • Generated Classified output data and Success statistics Report • Enhanced software modules to incorporate the GUI changes • Generated the data in the required format • Created Mouse Movement Biometrics Technical Paper • Created a User Manual to document use of the software • Created website to store the current application modules, tested results • Created training videos for the three applications in order to assist users in learning the system. • Uploaded the Technical Paper, User Manual as well as the mid term and final presentations on the website Mouse Movement Biometrics, Pace University, Fall'2007

  19. Mouse Movement Biometrics – Challenges During the initial enrollment process questions surrounding the application and how to access and run the application existed Difficulties in understanding the normalization process and using only two values (0 and 1) Getting the enhancements made for the existing MMSystems GUI to work in a single display window Mouse Movement Biometrics, Pace University, Fall'2007

  20. Mouse Movement Biometrics - Recommendations • Further enhancements to the data Capture module. • Work was started on adding new data fields to the Feature Extraction and Classifier modules but will need to be continued by succeeding teams. • While 100% accuracy is not probable, it seems more experiments need to be performed to see if there is a more consistent accuracy rate over time and from more generated data. • Subsequent teams should focus on developing the Data Capture GUI to randomize the buttons to provide more varied data Mouse Movement Biometrics, Pace University, Fall'2007

  21. Mouse Movement Biometrics – Recommendations cont’d • Subsequent teams can add more user characteristics to classify the user • Also, Subsequent teams can add more characteristics of the mouse such as right click or track wheel use • Finally, it would be optimal if the system was developed to be used online with a database backend. • This would allow for more data to be generated from a larger pool of users for further analysis and research . Mouse Movement Biometrics, Pace University, Fall'2007

  22. Questions/ Comments • http://utopia.csis.pace.edu/cs691/2007-2008/team1/default.htm Mouse Movement Biometrics, Pace University, Fall'2007