Abstract
This chapter describes a convolutional neural network (CNN)-based approach for overlapped fingerprint mask segmentation. The CNN classifies each image block within the overlapped fingerprint image into three classes—background (B), single fingerprint (S), and overlapped fingerprint (O). The proposed segmentation method has been successfully tested on three different datasets.
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Stojanović, B., Marques, O., Nešković, A. (2019). Machine Learning Based Segmentation of Overlapped Latent Fingerprints. In: Segmentation and Separation of Overlapped Latent Fingerprints. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-23364-8_4
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DOI: https://doi.org/10.1007/978-3-030-23364-8_4
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