Biometric Systems
dc.contributor.editor | Nanni, Loris | |
dc.contributor.editor | Berlin Brahnam, Sheryl | |
dc.date.accessioned | 2022-01-11T13:39:54Z | |
dc.date.available | 2022-01-11T13:39:54Z | |
dc.date.issued | 2021 | |
dc.identifier | ONIX_20220111_9783036511283_449 | |
dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/76714 | |
dc.description.abstract | Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study. | |
dc.language | English | |
dc.subject.classification | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues | en_US |
dc.subject.other | online signature verification | |
dc.subject.other | shape contexts | |
dc.subject.other | function features | |
dc.subject.other | SC-DTW | |
dc.subject.other | symbolic representation | |
dc.subject.other | two-stage method | |
dc.subject.other | finger features | |
dc.subject.other | multimodal recognition | |
dc.subject.other | local coding | |
dc.subject.other | Gabor filter | |
dc.subject.other | LGS | |
dc.subject.other | human identification | |
dc.subject.other | biomarker | |
dc.subject.other | ECG | |
dc.subject.other | machine learning | |
dc.subject.other | Physionet | |
dc.subject.other | Lviv Biometric Dataset | |
dc.subject.other | biometry | |
dc.subject.other | identification | |
dc.subject.other | bloodstream | |
dc.subject.other | image recognition | |
dc.subject.other | multi-biometrics | |
dc.subject.other | bit planes | |
dc.subject.other | block | |
dc.subject.other | mutual information | |
dc.subject.other | cross-device | |
dc.subject.other | dorsal hand vein recognition | |
dc.subject.other | person re-identification | |
dc.subject.other | superpixel | |
dc.subject.other | temporally aligned pooling | |
dc.subject.other | walking cycle | |
dc.subject.other | automatic recognition | |
dc.subject.other | face | |
dc.subject.other | voice | |
dc.subject.other | body motion | |
dc.subject.other | autism spectrum disorder (ASD) | |
dc.subject.other | assessment | |
dc.subject.other | intervention | |
dc.subject.other | curve similarity | |
dc.subject.other | curve similarity model | |
dc.subject.other | curve similarity transformation | |
dc.subject.other | similarity distance | |
dc.subject.other | segmentation matching | |
dc.subject.other | evolutionary computation | |
dc.subject.other | finger vein recognition | |
dc.subject.other | hand vein recognition | |
dc.subject.other | contactless acquisition device | |
dc.subject.other | public vascular pattern dataset | |
dc.subject.other | biometric recognition performance evaluation | |
dc.subject.other | face verification | |
dc.subject.other | optical correlation | |
dc.subject.other | Hausdorff distance | |
dc.subject.other | image classification | |
dc.subject.other | face detection | |
dc.subject.other | depth map ensemble | |
dc.subject.other | filtering | |
dc.subject.other | geometric deep learning | |
dc.subject.other | ear detection | |
dc.subject.other | structured prediction | |
dc.subject.other | semantic segmentation | |
dc.subject.other | rotation equivariance | |
dc.subject.other | Gaussian mixture model | |
dc.subject.other | superpixels | |
dc.subject.other | face recognition systems | |
dc.subject.other | person identification | |
dc.subject.other | biometric systems | |
dc.subject.other | survey | |
dc.subject.other | automatic signature verification | |
dc.subject.other | touch-screen sensor | |
dc.subject.other | data quality | |
dc.subject.other | enrollment phase | |
dc.subject.other | performance assessment | |
dc.subject.other | augmented signature | |
dc.subject.other | security enhancement | |
dc.subject.other | mobile conditions | |
dc.subject.other | biometric recognition | |
dc.subject.other | visible light iris images | |
dc.subject.other | image quality assessment | |
dc.subject.other | image covariates | |
dc.subject.other | quality filtering | |
dc.subject.other | vascular biometric recognition | |
dc.subject.other | wrist vein recognition | |
dc.subject.other | contactless dataset | |
dc.subject.other | pattern recognition | |
dc.subject.other | infrared camera | |
dc.subject.other | non-contact devices | |
dc.subject.other | Scale-Invariant Feature Transform (SIFT®) | |
dc.subject.other | Speeded Up Robust Features (SURF®) | |
dc.subject.other | Oriented FAST and Rotated BRIEF (ORB) | |
dc.subject.other | fingerprint | |
dc.subject.other | presentation attack detection | |
dc.subject.other | deep learning | |
dc.title | Biometric Systems | |
dc.type | book | |
oapen.identifier.doi | 10.3390/books978-3-0365-1129-0 | |
oapen.relation.isPublishedBy | 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 | |
oapen.relation.isbn | 9783036511283 | |
oapen.relation.isbn | 9783036511290 | |
oapen.pages | 352 | |
oapen.place.publication | Basel, Switzerland |
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