Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail
- Indbinding:
- Hardback
- Sideantal:
- 214
- Udgivet:
- 23. maj 2020
- Udgave:
- 12020
- Størrelse:
- 155x235x0 mm.
- Vægt:
- 585 g.
- Ukendt - mangler pt..
Forlænget returret til d. 31. januar 2025
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail
Fingerprints are among the most widely used biometric modalities and have been successfully applied in various scenarios. For example, in forensics, fingerprints serve as important legal evidence; and in civilian applications, fingerprints are used for access and attendance control as well as other identity services. Thanks to advances in three-dimensional (3D) and high-resolution imaging technology, it is now feasible to capture 3D or high-resolution fingerprints to provide extra information and go beyond the traditional features such as global ridge patterns and local ridge singularities used in conventional fingerprint recognition tasks.
This book presents the state of the art in the acquisition and analysis of 3D and high-resolution fingerprints. Based on the authors¿ research, this book focuses on advanced fingerprint recognition using 3D fingerprint features (i.e., finger shape, level 0 features) or high-resolution fingerprint features (i.e., ridge detail, level 3 features). It is a valuable resource for researchers, professionals and graduate students working in the field of computer vision, pattern recognition, security/biometrics practice, as well as interdisciplinary researchers.
This book presents the state of the art in the acquisition and analysis of 3D and high-resolution fingerprints. Based on the authors¿ research, this book focuses on advanced fingerprint recognition using 3D fingerprint features (i.e., finger shape, level 0 features) or high-resolution fingerprint features (i.e., ridge detail, level 3 features). It is a valuable resource for researchers, professionals and graduate students working in the field of computer vision, pattern recognition, security/biometrics practice, as well as interdisciplinary researchers.
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