Reprint

Advanced Biometrics with Deep Learning

Edited by
August 2020
210 pages
  • ISBN978-3-03936-698-9 (Hardback)
  • ISBN978-3-03936-699-6 (PDF)

This book is a reprint of the Special Issue Advanced Biometrics with Deep Learning that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

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.

Format
  • Hardback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
ECG identification; short-term ECG signals; HR-free resampling strategy; principal component analysis network; ECG-ID; audio replay attack; noise robustness; attention mechanism; long short-term memory; speech embedding; deep learning; speaker recognition; biometrics; finger-vein verification; deep learning; convolutional neural network; representation learning; person re-identification; misalignment; hierarchical feature aggregation; periocular recognition in the wild; convolutional neural network; colour-based local binary coded pattern; face detection; tiny faces; pre-identification mechanism; cascaded detector; deep learning; convolutional neural network; biometrics; face; deep learning; single sample per person; knowledge generalisation; Softmax; angular margin; ResNet; face recognition; face recognition; single sample per person; sample enriching; intrinsic decomposition; brain–computer interface (BCI); electroencephalogram (EEG); P300; electrocardiogram (ECG); biometric authentication; beat detection; depthwise separable convolution (DSC); ECG ID database; MIT-BIH database; n/a