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Deep Biometrics

Richard Jiang (Hrsg.), Chang-Tsun Li (Hrsg.), Christophe Rosenberger (Hrsg.), Danny Crookes (Hrsg.), Weizhi Meng (Hrsg.)

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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik

Beschreibung

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it “Deep Biometrics”. The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications.

  • Highlights the impact of deep learning over the field of biometrics in a wide area;
  • Exploits the deeper and wider background of biometrics, suchas privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.;
  • Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.

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    Schlagwörter

    Big data issues in Biometrics, Biometrics in Social Computing, Biometrics for Internet of things, Convolutional Neural networks, Medical/Healthcare Biometrics, Biometrics in Cybersecurity, Deep Learned Iris, Fingerprints, Palmprints, Deep Learned Biometric, Deep Face Detection, Recognition, Morphing, Cancellable Biometrics with Deep Learning