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Deep Learning for Biometrics

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Cover of 'Deep Learning for Biometrics'

Table of Contents

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    Book Overview
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    Chapter 1 The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning
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    Chapter 2 Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest
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    Chapter 3 CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection
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    Chapter 4 Latent Fingerprint Image Segmentation Using Deep Neural Network
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    Chapter 5 Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing
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    Chapter 6 Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks
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    Chapter 7 Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style
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    Chapter 8 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)
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    Chapter 9 Gender Classification from NIR Iris Images Using Deep Learning
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    Chapter 10 Deep Learning for Tattoo Recognition
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    Chapter 11 Learning Representations for Cryptographic Hash Based Face Template Protection
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    Chapter 12 Deep Triplet Embedding Representations for Liveness Detection
Attention for Chapter 8: 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)
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1 patent

Citations

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Readers on

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14 Mendeley
Summary Patents Dimensions citations
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Chapter title
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)
Chapter number 8
Book title
Deep Learning for Biometrics
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-61657-5_8
Book ISBNs
978-3-31-961656-8, 978-3-31-961657-5
Authors

Felix Juefei-Xu, Eshan Verma, Marios Savvides

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Student > Master 4 29%
Researcher 2 14%
Lecturer 1 7%
Unknown 3 21%
Readers by discipline Count As %
Computer Science 9 64%
Linguistics 1 7%
Engineering 1 7%
Unknown 3 21%

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