The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)

Design and Evaluation of the Human-Biometric Sensor Interaction Method


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Eric P. Kukula

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CERIAS TR 2008-26

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This research investigates the development and testing of the Human- Biometric Sensor Interaction Evaluation Method that used ergonomics, usability, and image quality criteria as explanatory variables of overall biometric system performance to evaluate swipe-based fingerprint recognition devices. The HBSI method was proposed because of questions regarding the thoroughness of traditional testing and performance evaluation metrics such as FTA, FTE, FAR, and FRR used in standardized evaluation methods; questioning if traditional metrics were acceptable enough to fully test and understand biometric systems, or determine if important data were not being collected. The Design and Evaluation of the Human-Biometric Sensor Interaction Method had four objectives: (a) analyze the literature to determine what influences the interaction of humans and biometric devices, (b) develop a conceptual model based on previous research, (c) design two alternative swipe fingerprint sensors, and (d) to compare how people interact with the commercial and designed swipe fingerprint sensors, to examine if changing the form factor improves the usability of the device in terms of the proposed HBSI evaluation method. Data was collected from 85 individuals over 3 visits that accounted for 33,394 interactions with the 4 sensors used. The HBSI Evaluation Method provided additional detail about how users interact with the devices collecting data on: image quality, number of detected minutiae, fingerprint image size, fingerprint image contrast, user satisfaction, task time, task completeness, user effort, number of assists; in addition to traditional biometric testing and reporting metrics of: acquisition failures (FTA), enrollment failures (FTE), and matching performance (FAR and FRR). Results from the HBSI Evaluation Method revealed that traditional biometric evaluations that focus on system-reported metrics are not providing sufficient reporting details. For example, matching performance for right and left index finger reported a FRR under 1% for all sensors at the operational point 0.1% FAR: UPEK (0.24%), PUSH (0.98%), PULL (0.36%), and large area (0.34%). However, the FTA rate was 11.28% and accounted for 3,768 presentations. From this research, two metrics previously unaccounted for and contained in the traditional FTA rate: Failure to Present (FTP) and False Failure to Present (FFTP) were created to better understand human interaction with biometric sensors and attribute errors accordingly. The FTP rate accounted for 1,187 of the 3,768 (31.5%) of interactions traditionally labeled as FTAs. The FFTP was much smaller at 0.35%, but can provide researchers further insight to help explain abnormal behaviors in matching rates, ROC and DET curves. In addition, traditional metrics of image quality and number of detected minutiae did not reveal a statistical difference across the sensors, however HBSI metrics of fingerprint image size and contrast did reveal a statistical difference, indicating the design of the PUSH sensor provided images of less gray level variation, while the PULL sensor provided images of larger pixel consistency during some of the data collection visits. The level of learning or habituation was also documented in this research through three metrics: task completion, Maximum User Effort (MUE), and the number of assists provided. All three reported the PUSH with the lowest rates, but improved the most over the visits, which was a function of learning how to use a “push”-based swipe sensor, as opposed to the “pull” swipe type. Overall the HBSI Evaluation Method provided the foundation for the future of biometric evaluations as it linked system feedback from erroneous interactions to the human-sensor interaction that caused the failure. This linkage will enable system developers and researchers the ability to re-examine the data to see if the errors are the result of the algorithm or human interaction that can be solved with revised training techniques, design modifications, or other adjustments in the future.




2008 – 8 – 1

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