Can point-cloud based neural networks learn fingerprint variability?

Subject- and environmental-specific variations affect the fingerprint recognition process. Quality metrics are capable of detecting and rating severe degradations. However, measuring natural variability, occurring during different fingerprint acquisitions, is not in the scope of these metrics. This work proposes the use of genuine comparison scores as a measure of variability. It is shown that the publicly available PLUS-MSL-FP dataset exhibits large natural variations which can be used to distinguish between different acquisition sessions. Furthermore, it is showcased that point-cloud (set) based neural networks are promising candidates for processing fingerprint imagery as they provide precise control over the input parameters. Experiments show that point-cloud based neural networks are capable of distinguishing between the different sessions in the PLUS-MSL-FP dataset solely based on FP minutiae locations.