Advances in testing and evaluation using Human-Biometric sensor interaction modules

This document discusses advances in testing and evaluating human-biometric sensor interaction using a new model. It describes gaps in traditional biometric testing, such as how users interact with systems. A new Human Biometric Sensor Interaction model is presented and has been tested on iris and fingerprint biometrics. The model has been expanded to more complex systems like border gates. Testing looks at how users interact with biometric systems in different environments and factors like throughput. The goal is to better test and evaluate systems without overburdening test facilities.

Technology

ADVANCES IN TESTING AND
EVALUATION USING HUMAN-BIOMETRIC
SENSOR
INTERACTION MODULES
BIOMETRICS 2014 – OCTOBER 21st

TALK TODAY
•Examine the advances in Human Biometric
Sensor Interaction model
• Current models
•Work ongoing in the Center

WHAT IS THE HUMAN BIOMETRIC SENSOR
INTERACTION MODEL

EVALUATION OF BIOMETRIC TESTING
• Traditional biometric testing
• Algorithm testing
• Well established metrics
• Well understood testing methodologies
• Operational testing
• Harder to do
• Access to environments
• Test methodologies dependent in some cases on the test

GAPS IN BIOMETRIC TESTING
•Training
• How do users get accustomed to devices?
• Can they remember how to use them?
• How do we provide good training to the users that
has a consistent message?

GAPS IN BIOMETRIC TESTING
• Accessibility
• How many people know people who have
problems with interacting with a biometric
systems?
• How do we deal with accessibility and usability
issues?
• Hearing and sight issues

GAPS IN BIOMETRIC TESTING
•Human Factors
• Testing and evaluating biometric systems by
looking at how the users interact with the system
• Are performance results different in an operational
environment than collect in a lab?
• Are these performance results due to the
environment?

GAPS IN BIOMETRIC TESTING
• Is the error always subject centric?
• The role of the device?
• The role of the operator?

HUMAN BIOMETRIC SENSOR INTERACTION
•Development of the model
• Initially tested the model on iris, fingerprint
• Then introduced the “Dynamic HBSI model” in
partnership with University of Kent, Canterbury
• On to more complex systems such as border
gates and mobile platforms

CHALLENGES / OPPORTUNITIES
• Challenges are that devices and biometric systems are:
• Now multifaceted
• Variety of form factors, deployments, applications
• In the hands of more customers
• They become more demanding
• Technology development cycle is short
• Opportunity is how to test and evaluate the interactions
without it becoming burdensome to a test facility

USE CASE: BORDER GATES
• Border gate – the interaction is more than just
presenting your biometric
• Heathrow last night….

CHALLENGES
•Token - the passport
•People traveling - potential incorrect passport
•Throughput issues - how to measure

NEW ADDITIONS TO THE HSBI MODEL
•Token
• False Claim
• Attack

TESTING AND EVALUATION RESEARCH
• HBSI and Border Gates
• Building a border gate in the
center to test various
technologies, including iris,
fingerprint, documents
• Throughput research
• Usability using the Kinect
• Novel performance metrics
such as the Stability Score
Index

TESTING AND EVALUATION RESEARCH
•Mobile devices are important as well, and we
continue to work in this area:
• Wild testing
• Illumination / Noise / Controlled environments
• Voice, face, signature, palm, and multi-factor
authentication

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This research focused on classifying Human-Biometric Sensor Interaction errors in real-time. The Kinect 2 was used as a measuring device to track the position and movements of the subject through a simulated border control environment. Knowing, in detail, the state of the subject ensures that the human element of the HBSI model is analyzed accurately. A network connection was established with the iris device to know the state of the sensor and biometric system elements of the model. Information such as detection rate, extraction rate, quality, capture type, and more metrics was available for use in classifying HBSI errors. A Federal Inspection Station (FIS) booth was constructed to simulate a U.S. border control setting in an International airport. The subjects were taken through the process of capturing iris and fingerprint samples in an immigration setting. If errors occurred, the Kinect 2 program would classify the error and saved these for further analysis.

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