The primary obstacle in realizing the full potential of finger crease biometrics is the accurate identification of deformed knuckle patterns, often resulting from completely contactless imaging. Current methods struggle significantly with this task, yet accurate matching is crucial for applications ranging from forensic investigations, such as child abuse cases, to surveillance and mobile security. To address this challenge, our study introduces the largest publicly available dataset of deformed knuckle patterns, comprising 805,768 images from 351 subjects. We also propose a novel framework to accurately match knuckle patterns, even under severe pose deformations, by recovering interpretable knuckle crease keypoint feature templates. These templates can dynamically uncover graph structure and feature similarity among the matched correspondences. Our experiments, using the most challenging protocols, illustrate significantly outperforming results for matching such knuckle images. For the first time, we present and evaluate a theoretical model to estimate the uniqueness of 2D finger knuckle patterns, providing a more interpretable and accurate measure of distinctiveness, which is invaluable for forensic examiners in prosecuting suspects.