Schuch, PatrickMay, Jan MarekBusch, ChristophBrömme, ArslanBusch, ChristophDantcheva, AntitzaRathgeb, ChristianUhl, Andreas2019-06-172019-06-172018978-3-88579-676-4https://dl.gi.de/handle/20.500.12116/23785The data origin (i.e. acquisition technique and acquisition mode) can have a significant impact on the appearance and characteristics of a fingerprint sample. This dataset bias might be challenging for processes like biometric feature extraction. Much effort can be put into data normalization or into processes able to deal with almost any input data. The performance of the former might suffer from this general applicability. The latter losses information by definition. If one is able to reliably identify the data origin of fingerprints, one will be able to dispatch the samples to specialized processes. Six methods of classification are evaluated for their capabilities to distinguish between fifteen different datasets. Acquisition technique and acquisition mode can be classified very accurately. Also, most of the datasets can be distinguished reliably.enfingerprint recognitionmachine learningdataset bias.Text/Conference Paper1617-5468