  This paper proposes a data driven model to predict the performance of a face
recognition system based on image quality features. We model the relationship
between image quality features (e.g. pose, illumination, etc.) and recognition
performance measures using a probability density function. To address the issue
of limited nature of practical training data inherent in most data driven
models, we have developed a Bayesian approach to model the distribution of
recognition performance measures in small regions of the quality space. Since
the model is based solely on image quality features, it can predict performance
even before the actual recognition has taken place. We evaluate the performance
predictive capabilities of the proposed model for six face recognition systems
(two commercial and four open source) operating on three independent data sets:
MultiPIE, FRGC and CAS-PEAL. Our results show that the proposed model can
accurately predict performance using an accurate and unbiased Image Quality
Assessor (IQA). Furthermore, our experiments highlight the impact of the
unaccounted quality space -- the image quality features not considered by IQA
-- in contributing to performance prediction errors.
