Fingerprint Minutiae Matching using Graph Neural Network with Geometric Invariant Features
We developed a novel graph-based fingerprint matching approach that is robust to geometric transformations such as rotation and translation. Unlike existing methods that rely on absolute minutiae coordinates and orientations, our method constructs geometry-invariant graphs using relative spatial relationships, enabling more consistent and accurate matching across varied fingerprint impressions.