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Segmentation and Separation of Overlapped Latent FingerprintsMachine Learning Based Segmentation of Overlapped Latent Fingerprints

Segmentation and Separation of Overlapped Latent Fingerprints: Machine Learning Based... [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Segmentation and Separation of Overlapped Latent FingerprintsMachine Learning Based Segmentation of Overlapped Latent Fingerprints

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
ISBN
978-3-030-23363-1
Pages
29 –34
DOI
10.1007/978-3-030-23364-8_4
Publisher site
See Chapter on Publisher Site

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.]

Published: Oct 23, 2019

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