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

## TL;DR

This paper introduces two new models that predict face recognition accuracy in computer vision systems based on video encoding parameters, validated across multiple datasets and applicable to various recognition metrics.

## Contribution

The paper presents novel models linking video encoding parameters to face recognition accuracy, validated with extensive experiments and applicable to different recognition metrics.

## Key findings

- Models accurately predict recognition accuracy under varying encoding conditions.
- Models are valid for deep learning and statistical face recognition methods.
- Insights into factors influencing model constants.

## Abstract

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.