A Bayesian approach to identifying faces from their IR facial
images amounts to testing of discrete hypotheses in presence of
nuisance variables such as pose, facial expression, and thermal
state. We propose an efficient, low-level technique for hypothesis
pruning, i.e. shortlisting high probability subjects, from given
observed image(s). (This subset can be further tested using some
detailed high-level model for eventual identification). Hypothesis
pruning is accomplished using wavelet decompositions (of the
observed images) followed by analysis of lower-order statistics of
the coefficients. Specifically, we filter infrared (IR) images
using bandpass filters and model the marginal densities of the
outputs via a parametric family that was introduced in
\cite{grenander-srivastava-clutter}. IR images are compared using
an $L^2$-metric computed directly from the parameters.  Results
from experiments on IR face identification and statistical pruning
are presented.