MARC details
| 000 -LEADER |
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| fixed length control field |
01940nam a2200289Ia 4500 |
| 000 - LEADER |
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| fixed length control field |
02231naaa 00301uu |
| 001 - CONTROL NUMBER |
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| control field |
https://directory.doabooks.org/handle/20.500.12854/68869 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20211222133725.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
211013s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783039366989 |
| 024 ## - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.3390/books978-3-03936-699-6 |
| 042 ## - AUTHENTICATION CODE |
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| Authentication code |
dc |
| 245 #0 - TITLE STATEMENT |
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| Title |
Advanced Biometrics with Deep Learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Basel, Switzerland |
| Name of publisher, distributor, etc. |
MDPI - Multidisciplinary Digital Publishing Institute |
| Date of publication, distribution, etc. |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 electronic resource (210 p.) |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
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| Terms governing access |
Open Access |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others. |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
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| Terms governing use and reproduction |
Creative Commons |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Jin, Andrew |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Jin, Andrew |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Leng, Lu |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Leng, Lu |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://directory.doabooks.org/handle/20.500.12854/68869">https://directory.doabooks.org/handle/20.500.12854/68869</a> |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://mdpi.com/books/pdfview/book/2636">https://mdpi.com/books/pdfview/book/2636</a> |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="www.oapen.org">www.oapen.org</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
E-Book |
