Handbook of Fingerprint Recognition
Davide Maltoni
Sold by buchversandmimpf2000, Emtmannsberg, BAYE, Germany
AbeBooks Seller since 23 January 2017
New - Hardcover
Condition: New
Ships from Germany to U.S.A.
Quantity: 2 available
Add to basketSold by buchversandmimpf2000, Emtmannsberg, BAYE, Germany
AbeBooks Seller since 23 January 2017
Condition: New
Quantity: 2 available
Add to basketNeuware -With their distinctiveness and stability over time, fingerprints continue to be the most widely used anatomical characteristic in systems that automatically recognize a person's identity.This fully updated third edition provides in-depth coverage of the state-of-the-art in fingerprint recognition readers, feature extraction, and matching algorithms and applications. Deep learning (resurgence beginning around 2012) has been a game changer for artificial intelligence and, in particular, computer vision and biometrics. Performance improvements (both recognition accuracy and speed) for most biometric modalities can be attributed to the use of deep neural networks along with availability of large training sets and powerful hardware. Fingerprint recognition has also been approached by deep learning, resulting in effective and efficient methods for automated recognition and for learning robust fixed-length representations. However, the tiny ridge details in fingerprints known as minutiae are still competitive with the powerful representations learned by huge neural networks trained on big data.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 548 pp. Englisch.
Seller Inventory # 9783030836238
With their distinctiveness and stability over time, fingerprints continue to be the most widely used anatomical characteristic in systems that automatically recognize a person's identity.
This fully updated third edition provides in-depth coverage of the state-of-the-art in fingerprint recognition readers, feature extraction, and matching algorithms and applications. Deep learning (resurgence beginning around 2012) has been a game changer for artificial intelligence and, in particular, computer vision and biometrics. Performance improvements (both recognition accuracy and speed) for most biometric modalities can be attributed to the use of deep neural networks along with availability of large training sets and powerful hardware. Fingerprint recognition has also been approached by deep learning, resulting in effective and efficient methods for automated recognition and for learning robust fixed-length representations. However, the tiny ridge details in fingerprints known as minutiae are still competitive with the powerful representations learned by huge neural networks trained on big data.Features & Benefits:
The revised edition of this must-read reference, written by leading international researchers, covers all critical aspects of fingerprint security system design and technology. It is an essential resource for all security and biometrics professionals, researchers, practitioners, developers, and systems administrators, and can serve as an easy-to-read reference for an undergraduate or graduate course on biometrics.
Dr. Davide Maltoni and Dr. Dario Maio are full Professors in the Department of Computer Science and Engineering at the University of Bologna, Italy. Dr. Anil K. Jain is a University Distinguished Professor at the Department of Computer Science and Engineering at Michigan State University, USA. Dr. Jianjiang Feng is an Associate Professor in the Department of Automation at Tsinghua University, China.
The authors’ extensive list of publications on biometrics include the Springer titles Encyclopedia of Biometrics, Introduction to Biometrics, Handbook of Face Recognition, Handbook of Biometrics, Handbook of Multibiometrics, Human Identification Based on Gait, Biometric Systems, Palmprint Authentication.
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