Date of Award

January 2015

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Technology Leadership and Innovation

First Advisor

Stephen Elliott

Committee Member 1

Richard Guest

Committee Member 2

Mathias Sutton

Abstract

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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