Camouflage garments can be associated with surveillance images of a crime scene even in the absence of unique wear marks or very high-quality images. However, the probability of an accidental association, or incidence rate, is significant. The present work describes and validates a method for estimating the incidence rate based on a statistical model of the garment manufacturing process. The model was developed primarily for use with the current U.S. Army Combat Uniform (ACU), but can be applied to any camouflage garment. Eight garment manufacturers were studied, and all sources of variation in the manufacturing process were characterized. The marking and spreading procedures were found to be dominant and consistent sources of variation. However, some sources of variation, in particular those because of human operators, were not consistent enough to accurately characterize. Sources of variation that could not be well-characterized were ignored in the statistical model, yielding a worst-case estimate that is an upper-bound to the true incidence rate. The model was evaluated for a variety of cases. Depending on the quality of the surveillance image, the manufacturing parameters, and the local population, incidence rates range from about 3% to negligibly small. The model was validated by returning to one manufacturer, and sampling a large number of completed garments and estimating empirical match probabilities. The empirical probabilities validated the estimates of the worst-case incidence rate and also demonstrated that typical incidence rates are significantly lower.

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