Advanced Biometrics with Deep Learning
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 extract...
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| Other Authors: | , |
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| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 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. |
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| Physical Description: | 1 electronic resource (210 p.) |
| ISBN: | books978-3-03936-699-6 9783039366989 9783039366996 |
| Access: | Open Access |