| 000 | 05332nam a22006015i 4500 | ||
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| 001 | 978-3-030-83624-5 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125433.0 | ||
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| 008 | 220704s2022 sz | s |||| 0|eng d | ||
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_a10.1007/978-3-030-83624-5 _2doi |
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| 100 | 1 |
_aMaltoni, Davide. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aHandbook of Fingerprint Recognition _h[electronic resource] / _cby Davide Maltoni, Dario Maio, Anil K. Jain, Jianjiang Feng. |
| 250 | _a3rd ed. 2022. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
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| 300 |
_aXXIV, 522 p. 269 illus., 82 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 505 | 0 | _aIntroduction -- Fingerprint sensing -- Fingerprint analysis and representation -- Fingerprint matching -- Fingerprint classification and indexing -- Latent fingerprint recognition -- Fingerprint synthesis -- Fingerprint individuality -- Securing fingerprint systems. | |
| 520 | _aWith 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 knownas minutiae are still competitive with the powerful representations learned by huge neural networks trained on big data. Features & Benefits: Reflects the progress made in automated techniques for fingerprint recognition over the past five decades Reviews the evolution of sensing technology: from bulky optical devices to in-display readers in smartphones Dedicates an entire new chapter to latent fingerprint recognition, which is nowadays feasible in “lights-out” mode Introduces classical and learning-based techniques for local orientation extraction, enhancement, and minutiae detection Provides an updated review of presentation-attack-detection techniques and their performance evaluation Discusses the evolution of minutiae matching from rich local descriptors to Minutiae Cylinder Code Presents the development of feature-based matching: from FingerCode to handcrafted textural features to deep features Reviews fingerprint synthesis, including recent Generative Adversarial Networks 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. Davide Maltoni is full professor in the Department of Computer Science (DISI) at the University of Bologna, where he also co-directs the Biometrics Systems Laboratory (BioLab). Dario Maio is full professor in the DISI and a co-director of the BioLab. Anil K. Jain is university distinguished professor in the Department of Computer Science and Engineering at Michigan State University. Jianjiang Feng is associate professor in the Department of Automation at Tsinghua University. | ||
| 650 | 0 | _aBiometric identification. | |
| 650 | 0 | _aPattern recognition systems. | |
| 650 | 0 | _aComputer crimes. | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aCriminology. | |
| 650 | 1 | 4 | _aBiometrics. |
| 650 | 2 | 4 | _aAutomated Pattern Recognition. |
| 650 | 2 | 4 | _aCybercrime. |
| 650 | 2 | 4 | _aMachine Learning. |
| 650 | 2 | 4 | _aCrime Control and Security. |
| 700 | 1 |
_aMaio, Dario. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 700 | 1 |
_aJain, Anil K. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 700 | 1 |
_aFeng, Jianjiang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030836238 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030836252 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030836269 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-83624-5 |
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