  In this dissertation, we present a generative model to capture the relation
between facial image quality features (like pose, illumination direction, etc)
and face recognition performance. Such a model can be used to predict the
performance of a face recognition system. Since the model is based solely on
image quality features, performance predictions can be done even before the
actual recognition has taken place thereby facilitating many preemptive action.
A practical limitation of such a data driven generative model is the limited
nature of training data set. To address this limitation, we have developed a
Bayesian approach to model the distribution of recognition performance measure
based on the number of match and non-match scores in small regions of the image
quality space. Random samples drawn from these models provide the initial data
essential for training the generative model. Experiment results based on six
face recognition systems operating on three independent data sets show that the
proposed performance prediction model can accurately predict face recognition
performance using an accurate and unbiased Image Quality Assessor (IQA).
Furthermore, our results show that variability in the unaccounted quality space
-- the image quality features not considered by the IQA -- is the major factor
causing inaccuracies in predicted performance.
