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Advanced Biometrics with Deep Learning

Abstract
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 bi

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: .,
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Basel, Switzerland, 2020
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NONE
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text
Media Type
computer
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online resource
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Siti
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Digital Object Identifier (DOI)
https://directory.doabooks.org/handle/20.500.12854/68869
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