Machine Learning Based Separation of Overlapped Latent Fingerprints
This chapter describes a machine learning based approach for overlapped fingerprint separation. The algorithm works in a block-based fashion: after producing an initial estimation of the orientation fields present in the overlapped fingerprint image, it uses a neural network to separate the mixed orientation fields, which are then post-processed to correct remaining errors and enhanced using the global orientation field enhancement model. The proposed separation method has been successfully tested on two different datasets.
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