Overall, the data generated during this research project supports fingerprint evidence as a valuable forensic tool for the identification of individuals. The model's performance shows that the spatial relationship between minutiae carries more evidential weight than their type or direction. Testing results also indicate that the AFIS component of the model directly enables the assignment of weight to fingerprint evidence without the need for the additional layer of complex statistical modeling required by the estimation of the probability distributions of fingerprint features. In fact, it seems that the AFIS component is more sensitive to the sub-population effects than the other components of the model. Contrary to previous models (generative and score-based models), the proposed model estimates the probability distributions of spatial relationships, directions, and types of minutiae observed on fingerprints for any given fingermark. The model relies on an AFIS algorithm provided by 3M Cogent and a dataset of more than 4,000,000 fingerprints to represent a sample from a relevant population of potential sources. The model's performance was tested using several hundreds of minutiae configurations observed on a set of 565 fingermarks. In particular, the effects of various sub-populations of fingers (i.e., finger number, finger general pattern) on the expected evidential value of the test configurations were investigated. (Publisher abstract modified)