Statistical hypothesis pruning for identifying faces from infrared images

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, lowlevel 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 [11]. IR images are compared using an L2-metric computed directly from the parameters. Results from experiments on IR face identification and statistical pruning are presented. Keywords—: infrared image analysis, face identification, Bessel K forms, image statistics, hypothesis pruning.