  A major challenge facing Computer Vision systems is providing the ability to
accurately detect threats and recognize subjects and/or objects under
dynamically changing network conditions. We propose two novel models that
characterize the face recognition accuracy in terms of video encoding
parameters. Specifically, we model the accuracy in terms of video resolution,
quantization, and actual bit rate. We validate the models using two distinct
video datasets and a large image dataset by conducting 1, 668 experiments that
involve simultaneously varying combinations of encoding parameters. We show
that both models hold true for the deep learning and statistical based face
recognition. Furthermore, we show that the models can be used to capture
different accuracy metrics, specifically the recall, precision, and F1-score.
Ultimately, we provide meaningful insights on the factors affecting the
constants of each proposed model.
