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Produktbild: Deep Learning for Biometrics

Deep Learning for Biometrics

Aus der Reihe Advances in Computer Vision and Pattern Recognition

167,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.05.2018

Abbildungen

XXXI, 312 p. 117 illus., 96 illus. in color.

Herausgeber

Bir Bhanu + weitere

Verlag

Springer

Seitenzahl

312

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

585 g

Auflage

Softcover reprint of the original 1st edition 2017

Sprache

Englisch

ISBN

978-3-319-87128-8

Beschreibung

Rezension

“This book, which covers different deep learning neural architectures for solving an extended set of problems in the area of biometrics, is sure to catch the attention of scholars and researchers working in the field.” (CK Raju, Computing Reviews, February, 2019)


Portrait

Dr. Bir Bhanu  is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles  Video Bioinformatics ,  Distributed Video Sensor Networks , and  Human Recognition at a Distance in Video .

Dr. Ajay Kumar  is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.05.2018

Abbildungen

XXXI, 312 p. 117 illus., 96 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

312

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

585 g

Auflage

Softcover reprint of the original 1st edition 2017

Sprache

Englisch

ISBN

978-3-319-87128-8

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Deep Learning for Biometrics

  • Part I: Deep Learning for Face Biometrics .- The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning.- Real-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing Forest.- CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection.-  Part II: Deep Learning for Fingerprint, Fingervein and Iris Recognition .- Latent Fingerprint Image Segmentation Using Deep Neural Networks.- Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing.- Iris Segmentation Using Fully Convolutional Encoder-Decoder Networks.-  Part III: Deep Learning for Soft Biometrics .- Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style.- DeepGender2: A Generative Approach Toward Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN).- Gender Classification from NIR Iris Images Using Deep Learning.- Deep Learning for Tattoo Recognition.-  Part IV: Deep Learning for Biometric Security and Protection .- Learning Representations for Cryptographic Hash Based Face Template Protection.- Deep Triplet Embedding Representations for Liveness Detection.