Date of Award

5-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Engineering and Sciences

First Advisor

Michael King

Second Advisor

Jigyna Patel

Third Advisor

Anthony O. Smith

Fourth Advisor

Philip J. Bernhard

Abstract

Recent investigations have demonstrated that it might be challenging to identify faces in the images taken using a long-distance camera. A face seems blurry in these images because of the presence of atmospheric turbulence. To examine how atmospheric turbulence impacts face biometrics, we establish a simulated environment that exhibits different degrees of turbulence. We employed the Rytov Variance, which relies on the distance and refractive index, C2 n, to get various turbulence levels. We used the LRFID dataset to carry out the study, which is a collection of photos and videos taken in the field and in a controlled setting. Using the metadata extracted from the LRFID dataset, we were able to determine the three extreme C2 n values and their accompanying Rytov values for different levels of turbulence while labeling them as weak, moderate, and strong for two different distances of 300m and 500m. With the simulated dataset, we see how atmospheric turbulence affects biometrics using two biometric software, one open-source algorithm, ArcFace, and another commercial matcher.

Comments

Copyright held by author

Download

Share

COinS