Date of Award
Master of Science (MS)
Computer Engineering and Sciences
Anthony O. Smith
Philip J. Bernhard
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.
Jain, Muskan, "Assessing the Effect of Atmospheric Turbulence on Long-Range Face Recognition Accuracy" (2023). Theses and Dissertations. 1267.