(2013) Automatic Detection of Biometrics Transaction Times

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Presented at The 8th International Conference on Information Technology and Applications (ICITA 2013), Sydney Australia, July 1 - July 4 2013. …

Presented at The 8th International Conference on Information Technology and Applications (ICITA 2013), Sydney Australia, July 1 - July 4 2013.

The purpose of this paper is to illustrate the automatic detection of biometric transaction times using hand geometry as the modality of interest. Video recordings were segmented into individual frames and processed through a program to automatically detect interactions between the user and the system. Results include a mean enrollment time of 15.860 seconds and a mean verification time of 2.915 seconds.

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  • 1. Sidlauskas, D., Tamer, S., (2007). Hand Geometry Recognition. Handbook of Biometrics. Springer US. doi: 10.1007/978-0-387-71041-9_5
  • 2. Liu, S., & Silverman, M. (2001). A practical guide to biometric security technology. IT Professional, 3(1), 27–32. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=899930
  • 3. Sanchez-Reillo, R., & Gonzalez-Marcas, A. (2000). Access control system with hand geometry verification and smart cards. Aerospace and Electronic Systems Magazine, IEEE, 15(45), 45–48. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8256714. Tamer, S., Elliott, S., (2009, July) Time and Attendance. Encyclopedia of Biometrics. Springer US. doi:10.1007/978-0-387-73003-5_114
  • 5. Poh, N., Bourlai, T., & Kittler, J. (2010). A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms. Pattern Recognition, 43(3), 1094–1105. doi:10.1016/j.patcog.2009.09.011
  • 6. Bailey, B. P., Konstan, J. a., & Carlis, J. V. (2000). Measuring the effects of interruptions on task performance in the user interface. SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. “Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions” (Cat. No.00CH37166), 2, 757–762. doi:10.1109/ICSMC.2000.885940
  • 7. Tinsley, H. E. A., & Weiss, D. J. (1975). Interrater Reliability and Agreement of Subjective Judgments. Journal of Counseling Psychology, 22(4), 358–376.
  • 8. Lazarick, R. T., Kukula, E. P., & Elliott, S. J. (2009, July). Operational Times. Encyclopedia of Biometrics. Springer US. doi:10.1007/978-0-387-73003-5_114
  • This process begins when the PIN is enteredFor hand geometry verification only one attempt is given in the transaction.
  • In verification, one attempt contains one presentation.
  • Hand geometry enrollment is made up of 3 presentations of sufficient quality.
  • Signified by the lights on the hand geometry machine changing color. This may happen many times within a presentation to the systemInteraction occurs between the subject and the system.Instructions should be provided to the subject before the first interaction begins.
  • Video coding provides consistency.Bullet 2 is a rehash from the introduction

Transcript

  • 1. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation AUTOMATIC DETECTION OF BIOMETRIC TRANSACTION TIMES MICHAEL BROCKLY STEPHEN ELLIOTT PH.D.
  • 2. HAND GEOMETRY • Measures length, width, and thickness of hand [1] • Engages 1:1 matching by entering a Personal Identification Number (PIN) [1]
  • 3. USES • Joins a PIN number with the security of biometric verification • Commonly used in time and attendance and access control • Hand geometry has proven to be very popular in time and attendance recording [2]
  • 4. BENEFITS • Hand geometry functions as a medium cost system with fast computational speeds, low template size, and good ease of use [3] • The convenience of hand geometry stems from the fact that users cannot lose or forget their biometric credential [4]
  • 5. TIME ON TASK • Computational speed is always a primary concern • Slow throughput times may eliminate the cost savings proposed by device installation • Higher costs are associated with a higher time to acquire or process a biometric sample [5]
  • 6. VIDEO CODING • Previous studies suggest video recording in order to capture subject time on task [6] • Time consuming process to manually record timing data • Potential for errors and inconsistencies
  • 7. INTERRATER RELIABILITY • Represents the degree to which the ratings of different judges are proportional when expressed as deviations from their means [7] • Not all video coders will report the same result
  • 8. OPERATIONAL TIMES • Previous research has suggested models for biometric transaction times • Biometric transaction time includes: – Subject interaction time – Biometric subsystem processing time – Biometric subsystem decision time – External control access time
  • 9. OPERATIONAL TIME MODEL [8]
  • 10. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation EXPERIMENTAL SETUP
  • 11. DEVICE • Ingersoll Rand Handkey II • Hand geometry biometric device
  • 12. CAMERA • Logitech HD Pro C910 Webcam – 1080p recording • Used to video record interaction changes on hand geometry device
  • 13. SETUP • Camera placed 24 cm above hand geometry machine • Device placed 90 cm above ground level
  • 14. EXPERIMENT • Hand geometry data was collected as part of a larger multi-modal study • This data collection included 35 subjects • Other modalities collected include fingerprint, iris, face, signature, and palm vein
  • 15. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation VIDEO ANALYSIS
  • 16. USES • An automated tool was created to analyze the videos • Analyzes videos to 15 frames per second • Detects light changes on device as pixel color thresholds are crossed • Writes results without human coder
  • 17. CROPPING
  • 18. FRAME SELECTION
  • 19. LIGHT SELECTION
  • 20. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation TRANSACTION TIME USE CASE – HAND GEOMETRY
  • 21. SYSTEM READY • System ready
  • 22. USER MAKES A CLAIM OR PRESENTS AN IDENTITY • User enters PIN
  • 23. SAMPLE ACQUISITION • Lights all on
  • 24. SAMPLE ACQUISITION • User places hand
  • 25. SAMPLE ACQUISITION • Lights change
  • 26. SAMPLE ACQUISITION • Lights continue to change
  • 27. SAMPLE ACQUISITION • Lights all off
  • 28. BIOMETRIC SUBSYSTEM DECISION • Green or red light
  • 29. EXTERNAL CONTROL ACTION • Not used in this study • External control may be opening door or granting access to system
  • 30. COMBINATION OF MODELS
  • 31. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation TERMINOLOGY
  • 32. CONFLICTING TERMINOLOGY • Along with the model, we include specific terminology and emphasize the linkages between the two versions
  • 33. TRANSACTION • The sequence of attempts to the system on the part of the user for the purpose of enrollment, verification or identification • This definition follows ISO/IEC FCD 19795-1’s definition of a transaction
  • 34. ATTEMPT • The submission of one (or a sequence of) biometric samples to the system on the part of the user – One or more attempts as allowed by the biometric system will create one transaction • This definition follows ISO/IEC FCD 19795-1’s definition of an attempt
  • 35. PRESENTATION • The submission of a single biometric sample to the system on the part of the user – One or more presentations as allowed by the biometric system will create one attempt • This definition follows ISO/IEC FCD 19795-1’s definition of a presentation
  • 36. INTERACTION • The action(s) that take place within a presentation – One or more interactions will create one presentation • This definition conflicts with ISO/IEC FCD 19795-1’s definition as “a sequence of transactions”
  • 37. HIERARCHY Transaction Attempt 1 Presentation 1 Interaction 1 Attempt 2 Presentation 2 Interaction 2 ……… Attempt N Presentation N Interaction N
  • 38. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation RESULTS
  • 39. ENROLLMENT TIME
  • 40. INDIVIDUAL VERIFICATION TIME
  • 41. VERIFICATION TIME
  • 42. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation CONCLUSIONS
  • 43. BENEFITS OF AUTOMATIC CODING • Eliminates need for manual video coding • Video coding is a time consuming task and has potential for errors • Goal is to create a consistent measure of biometric transactions
  • 44. LESSONS LEARNED • Experimental test conditions are not always stable – Due to cameras being moved/bumped, they will not always be in the same location • Original version of software did not take this into account • Second version allowed the area of interest to be selected based on a frame of the video
  • 45. RELATION TO HBSI • This experiment addresses the need to automate the error detection in the Human Biometric Sensor Interaction (HBSI) model • HBSI is concerned with classifying correct and incorrect presentations into quantifiable metrics
  • 46. HBSI ERROR METRICS
  • 47. HBSI • This philosophy can be duplicated to record these error metrics • Ex. 1 If all lights are extinguished and green light is shown, SPS • Ex 2. If all lights remain on until system time out and red light is shown, FTD
  • 48. NEXT STEPS • Methodology can be replicated for other modalities as well • Any system that provides feedback can be video recorded and analyzed • Automatically code HBSI error metrics
  • 49. CONTACT INFORMATION • Michael Brockly – mbrockly@purdue.edu • Stephen Elliott Ph.D. – elliott@purdue.edu
  • 50. BIOMETRICS LAB Biometric Standards, Performance and Assurance Laboratory Department of Technology, Leadership and Innovation QUESTIONS?
  • 51. REFERENCES [1] Sidlauskas, D., Tamer, S., (2007). Hand Geometry Recognition. Handbook of Biometrics. Springer US. doi: 10.1007/978-0-387- 71041-9_5 [2] Liu, S., & Silverman, M. (2001). A practical guide to biometric security technology. IT Professional, 3(1), 27–32. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=899930 [3] Sanchez-Reillo, R., & Gonzalez-Marcas, A. (2000). Access control system with hand geometry verification and smart cards. Aerospace and Electronic Systems Magazine, IEEE, 15(45), 45–48. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=82 5671 [4] Tamer, S., Elliott, S., (2009, July) Time and Attendance. Encyclopedia of Biometrics. Springer US. doi:10.1007/978-0-387- 73003-5_114
  • 52. REFERENCES [5] Poh, N., Bourlai, T., & Kittler, J. (2010). A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms. Pattern Recognition, 43(3), 1094–1105. doi:10.1016/j.patcog.2009.09.011 [6] Bailey, B. P., Konstan, J. a., & Carlis, J. V. (2000). Measuring the effects of interruptions on task performance in the user interface. SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. “Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions” (Cat. No.00CH37166), 2, 757–762. doi:10.1109/ICSMC.2000.885940 [7] Reliability and Agreement of Subjective Judgments. Journal of Counseling Psychology, 22(4), 358–376. [8] Lazarick, R. T., Kukula, E. P., & Elliott, S. J. (2009, July). Operational Times. Encyclopedia of Biometrics. Springer US. doi:10.1007/978-0-387-73003-5_114