Coverart for item
The Resource Deep learning for biometrics, Bir Bhanu, Ajay Kumar, editors, (electronic resource)

Deep learning for biometrics, Bir Bhanu, Ajay Kumar, editors, (electronic resource)

Label
Deep learning for biometrics
Title
Deep learning for biometrics
Statement of responsibility
Bir Bhanu, Ajay Kumar, editors
Contributor
Subject
Language
eng
Member of
Dewey number
  • 006.2/48
  • 004
Index
no index present
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Bhanu, Bir
  • Kumar, Ajay
Series statement
Advances in Computer Vision and Pattern Recognition
http://library.link/vocab/subjectName
  • Biometric identification
  • Machine learning
Label
Deep learning for biometrics, Bir Bhanu, Ajay Kumar, editors, (electronic resource)
Instantiates
Publication
Note
Deep Learning for Fingerprint, Fingervein and Iris Recognition
Antecedent source
unknown
Color
multicolored
Contents
  • Preface; Outline of the Book and Chapter Synopsis; Challenges for the Future; Acknowledgements; Contents; List of Figures; List of Tables; Deep Learning for Face Biometrics; 1 The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning; 1.1 The Functional Characteristics and Organization of the Ventral Face Network in the Human Brain; 1.1.1 Functional Characteristics of the Ventral Face Network; 1.2 The Neural Architecture and Connections of the Ventral Face Network
  • 1.2.1 The Functional Organization of the Face Network Is Consistent Across Participants1.2.2 The Cytoarchitecture of Face-Selective Regions; 1.2.3 White Matter Connections of the Ventral Face Network; 1.3 Computations by Population Receptive Fields in the Ventral Face Network; 1.3.1 pRF Measurements Reveal a Hierarchical Organization of the Face Network; 1.3.2 Attention Modulates pRF Properties, Enhancing Peripheral Representations Where Visual Acuity Is the Worst; 1.4 Eyes to the Future: Computational Insights from Anatomical and Functional Features of the Face Network
  • 1.4.1 What Is the Computational Utility of the Organized Structure of the Cortical Face Network?1.4.2 What Can Deep Convolutional Networks Inform About Computational Strategies of the Brain?; 1.5 Conclusions; References; 2 Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest; 2.1 Introduction; 2.2 Related Work; 2.3 CNHF with Multiple Convolution CNN; 2.4 Learning Face Representation via Boosted Hashing Forest; 2.4.1 Boosted SSC, Forest Hashing and Boosted Hashing Forest; 2.4.2 BHF: Objective-Driven Recurrent Coding
  • 2.4.3 BHF: Learning Elementary Projection via RANSAC Algorithm2.4.4 BHF: Boosted Hashing Forest; 2.4.5 BHF: Hashing Forest as a Metric Space; 2.4.6 BHF: Objective Function for Face Verification and Identification; 2.4.7 BHF Implementation for Learning Face Representation; 2.5 Experiments; 2.5.1 Methodology: Learning and Testing CNHF; 2.5.2 Hamming Embedding: CNHL Versus CNN, BHF Versus Boosted SSC; 2.5.3 CNHF: Performance w.r.t. Depth of Trees; 2.5.4 CNHL and CNHF Versus Best Methods on LFW; 2.6 Conclusion and Discussion; References
  • 3 CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection3.1 Introduction; 3.2 Related Work; 3.3 Background in Deep Convolution Nets; 3.3.1 Region-Based Convolution Neural Networks; 3.3.2 Limitations of Faster R-CNN; 3.3.3 Other Face Detection Method Limitations; 3.4 Contextual Multi-Scale R-CNN; 3.4.1 Identifying Tiny Faces; 3.4.2 Integrating Body Context; 3.4.3 Information Fusion; 3.4.4 Implementation Details; 3.5 Experiments; 3.5.1 Experiments on WIDER FACE Dataset; 3.5.2 Experiments on FDDB Face Database; 3.6 Conclusion and Future Work; References
Control code
ocn999658478
Dimensions
unknown
Extent
1 online resource (329 p.)
File format
unknown
Form of item
online
Isbn
9783319616575
Level of compression
unknown
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)999658478
Label
Deep learning for biometrics, Bir Bhanu, Ajay Kumar, editors, (electronic resource)
Publication
Note
Deep Learning for Fingerprint, Fingervein and Iris Recognition
Antecedent source
unknown
Color
multicolored
Contents
  • Preface; Outline of the Book and Chapter Synopsis; Challenges for the Future; Acknowledgements; Contents; List of Figures; List of Tables; Deep Learning for Face Biometrics; 1 The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning; 1.1 The Functional Characteristics and Organization of the Ventral Face Network in the Human Brain; 1.1.1 Functional Characteristics of the Ventral Face Network; 1.2 The Neural Architecture and Connections of the Ventral Face Network
  • 1.2.1 The Functional Organization of the Face Network Is Consistent Across Participants1.2.2 The Cytoarchitecture of Face-Selective Regions; 1.2.3 White Matter Connections of the Ventral Face Network; 1.3 Computations by Population Receptive Fields in the Ventral Face Network; 1.3.1 pRF Measurements Reveal a Hierarchical Organization of the Face Network; 1.3.2 Attention Modulates pRF Properties, Enhancing Peripheral Representations Where Visual Acuity Is the Worst; 1.4 Eyes to the Future: Computational Insights from Anatomical and Functional Features of the Face Network
  • 1.4.1 What Is the Computational Utility of the Organized Structure of the Cortical Face Network?1.4.2 What Can Deep Convolutional Networks Inform About Computational Strategies of the Brain?; 1.5 Conclusions; References; 2 Real-Time Face Identification via Multi-convolutional Neural Network and Boosted Hashing Forest; 2.1 Introduction; 2.2 Related Work; 2.3 CNHF with Multiple Convolution CNN; 2.4 Learning Face Representation via Boosted Hashing Forest; 2.4.1 Boosted SSC, Forest Hashing and Boosted Hashing Forest; 2.4.2 BHF: Objective-Driven Recurrent Coding
  • 2.4.3 BHF: Learning Elementary Projection via RANSAC Algorithm2.4.4 BHF: Boosted Hashing Forest; 2.4.5 BHF: Hashing Forest as a Metric Space; 2.4.6 BHF: Objective Function for Face Verification and Identification; 2.4.7 BHF Implementation for Learning Face Representation; 2.5 Experiments; 2.5.1 Methodology: Learning and Testing CNHF; 2.5.2 Hamming Embedding: CNHL Versus CNN, BHF Versus Boosted SSC; 2.5.3 CNHF: Performance w.r.t. Depth of Trees; 2.5.4 CNHL and CNHF Versus Best Methods on LFW; 2.6 Conclusion and Discussion; References
  • 3 CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection3.1 Introduction; 3.2 Related Work; 3.3 Background in Deep Convolution Nets; 3.3.1 Region-Based Convolution Neural Networks; 3.3.2 Limitations of Faster R-CNN; 3.3.3 Other Face Detection Method Limitations; 3.4 Contextual Multi-Scale R-CNN; 3.4.1 Identifying Tiny Faces; 3.4.2 Integrating Body Context; 3.4.3 Information Fusion; 3.4.4 Implementation Details; 3.5 Experiments; 3.5.1 Experiments on WIDER FACE Dataset; 3.5.2 Experiments on FDDB Face Database; 3.6 Conclusion and Future Work; References
Control code
ocn999658478
Dimensions
unknown
Extent
1 online resource (329 p.)
File format
unknown
Form of item
online
Isbn
9783319616575
Level of compression
unknown
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)999658478

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
Processing Feedback ...