A Bayesian model for predicting face recognition performance using image quality

TL;DR: A Bayesian approach to model the relation between image quality and corresponding face recognition performance and it is shown that this model can accurately aggregate verification samples into groups for which the verification performance varies fairly consistently.

Abstract: Quality of a pair of facial images is a strong indicator of the uncertainty in decision about identity based on that image pair. In this paper, we describe a Bayesian approach to model the relation between image quality (like pose, illumination, noise, sharpness, etc) and corresponding face recognition performance. Experiment results based on the MultiPIE data set show that our model can accurately aggregate verification samples into groups for which the verification performance varies fairly consistently. Our model does not require similarity scores and can predict face recognition performance using only image quality information. Such a model has many applications. As an illustrative application, we show improved verification performance when the decision threshold automatically adapts according to the quality of facial images.