img Leseprobe Leseprobe

Segmentation and Separation of Overlapped Latent Fingerprints

Algorithms, Techniques, and Datasets

Oge Marques, Branka Stojanović, Aleksandar Nešković, et al.

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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Anwendungs-Software

Beschreibung

This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.

By offering a structured overview of the most important approaches currently available, putting them in perspective, and suggesting numerous resources for further exploration, this book gives its readers a clear path forlearning new topics and engaging in related research. Written from a technical perspective, and yet using language and terminology accessible to non-experts, it describes the technologies, introduces relevant datasets, highlights the most important research results in each area, and outlines the most challenging open research questions.

This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information.

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Schlagwörter

Overlapped fingerprint separation, Neural Networks, Convolutional Neural Networks, Machine Learning, Biometrics, Fingerprint datasets, Overlapped fingerprint segmentation, Deep Learning, Forensics, Latent fingerprints, Fingerprint verification