(2013) automatic detection of biometrics transaction times

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

AUTOMATIC DETECTION OF BIOMETRIC TRANSACTION TIMESMICHAEL BROCKLYSTEPHEN ELLIOTT PH.D.

HAND GEOMETRY

• Measures length, width, and thickness of hand [1]

• Engages 1:1 matching by entering a Personal Identification Number (PIN)

[1]

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]

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]

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]

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

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

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

OPERATIONAL TIME MODEL

[8]

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

EXPERIMENTAL SETUP

DEVICE

• Ingersoll Rand Handkey II

• Hand geometry biometric device

CAMERA

• Logitech HD Pro C910 Webcam– 1080p recording

• Used to video record interaction changes on hand geometry device

SETUP

• Camera placed 24 cm above hand geometry machine

• Device placed 90 cm above ground level

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

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

VIDEO ANALYSIS

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

CROPPING

FRAME SELECTION

LIGHT SELECTION

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

TRANSACTION TIME USE CASE – HAND GEOMETRY

SYSTEM READY

• System ready

USER MAKES A CLAIM OR PRESENTS AN IDENTITY

• User enters PIN

SAMPLE ACQUISITION

• Lights all on

SAMPLE ACQUISITION

• User places hand

SAMPLE ACQUISITION

• Lights change

SAMPLE ACQUISITION

• Lights continue to change

SAMPLE ACQUISITION

• Lights all off

BIOMETRIC SUBSYSTEM DECISION

• Green or red light

EXTERNAL CONTROL ACTION

• Not used in this study• External control may be opening door or

granting access to system

COMBINATION OF MODELS

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

TERMINOLOGY

CONFLICTING TERMINOLOGY

• Along with the model, we include specific terminology and emphasize the linkages between the two versions

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

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

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

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”

HIERARCHY

Transaction

Attempt 1Presentation 1Interaction 1

Attempt 2Presentation 2Interaction 2

………

Attempt NPresentation NInteraction N

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

RESULTS

ENROLLMENT TIME

INDIVIDUAL VERIFICATION TIME

VERIFICATION TIME

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

CONCLUSIONS

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

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

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

HBSI ERROR METRICS

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

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

CONTACT INFORMATION

• Michael Brockly– mbrockly@purdue.edu

• Stephen Elliott Ph.D.– elliott@purdue.edu

BIOMETRICS LABBiometric Standards, Performance and Assurance LaboratoryDepartment of Technology, Leadership and Innovation

QUESTIONS?

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

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