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

QRMODA and BRMODA: Novel Models for Face Recognition Accuracy in Computer Vision Systems with Adapted Video Streams