Deep Learning-Based Face Analytics.

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Bibliographic Details
Main Author: Ratha, Nalini K.
Other Authors: Patel, Vishal M., Chellappa, Rama
Format: e-Book
Language:English
Published: Cham : Springer International Publishing AG, 2021.
Edition:1st ed.
Series:Advances in Computer Vision and Pattern Recognition Series
Subjects:
Human face recognition (Computer science).
Electronic books.
Online Access:Full-text access
Table of Contents:
  • Intro
  • Contents
  • 1 Deep CNN Face Recognition: Looking at the Past and the Future
  • 1.1 Synonyms
  • 1.2 Introduction
  • 1.3 Datasets
  • 1.4 Face Detection
  • 1.5 Loss Functions
  • 1.6 Face Verification and Identification Using CNNs
  • 1.7 Open Problems
  • References
  • 2 Face Segmentation, Face Swapping, and How They Impact Face Recognition
  • 2.1 Introduction
  • 2.2 Related Work
  • 2.2.1 Face Segmentation
  • 2.2.2 Face Swapping
  • 2.3 Swapping Faces in Unconstrained Images
  • 2.3.1 Fitting 3D Face Shapes
  • 2.3.2 Deep Face Segmentation
  • 2.3.3 Face Swapping and Blending
  • 2.4 Experiments
  • 2.4.1 Face Segmentation Evaluations
  • 2.4.2 Qualitative Face Swapping Results
  • 2.4.3 Qualitative Ablation Study
  • 2.4.4 Limitations of Our System
  • 2.5 The effects of Swapping on Recognition
  • 2.5.1 Face Verification System
  • 2.5.2 Inter-Subject Swapping Verification Protocols
  • 2.5.3 Inter-Subject Swapping Results
  • 2.5.4 Intra-Subject Swapping Verification Protocols and Results
  • 2.6 Conclusions
  • References
  • 3 Disentangled Representation Learning and Its Application to Face Analytics
  • 3.1 Introduction
  • 3.2 Application 1: Facial Landmark Tracking
  • 3.2.1 Related Works
  • 3.2.2 Our Approach: RED-Net
  • 3.2.3 Experiments
  • 3.3 Application 2: Learning Facial Representations for Inference and Generation
  • 3.3.1 Related Works
  • 3.3.2 Our Approach: CR-GAN
  • 3.3.3 Results: Multi-view Facial Image Generation
  • 3.3.4 Results: Conditional Facial Attribute Manipulation
  • 3.4 Conclusion
  • References
  • 4 Learning 3D Face Morphable Model from In-the-Wild Images
  • 4.1 Introduction
  • 4.2 Prior Work
  • 4.2.1 Linear 3DMM
  • 4.2.2 Improving Linear 3DMM
  • 4.2.3 2D Face Alignment
  • 4.2.4 3D Face Reconstruction
  • 4.2.5 Unsupervised Learning in 3DMM
  • 4.3 The Proposed Nonlinear 3DMM
  • 4.3.1 Conventional Linear 3DMM
  • 4.3.2 Nonlinear 3DMM.
  • 4.4 Improving Model Fidelity
  • 4.4.1 Nonlinear 3DMM with Proxy and Residual
  • 4.4.2 Global-Local-Based Network Architecture
  • 4.5 Experimental Results
  • 4.5.1 Ablation Study
  • 4.5.2 Expressiveness
  • 4.5.3 Representation Power
  • 4.5.4 Applications
  • 4.6 Conclusions
  • References
  • 5 Deblurring Face Images Using Deep Networks
  • 5.1 Deep Semantic Face Deblurring
  • 5.2 Deblurring Via Structure Generation and Detail Enhancement
  • 5.3 Uncertainty Guided Multi-stream Semantic Networks
  • 5.3.1 Image Deblurring Network
  • 5.3.2 Semantic Segmentation Network (SN)
  • 5.3.3 Base Network (BN)
  • 5.3.4 UMSN Network
  • 5.3.5 Loss for UMSN
  • 5.3.6 Uncertainty Guidance
  • 5.3.7 Experimental Results
  • 5.4 Conclusion
  • References
  • 6 Blind Super-resolution of Faces for Surveillance
  • 6.1 Introduction
  • 6.2 Related Works
  • 6.3 Learning Invariant Features for Faces
  • 6.4 Network Architecture
  • 6.4.1 Encoder-Decoder
  • 6.4.2 GAN for Feature Mapping
  • 6.4.3 Loss Function
  • 6.4.4 Training
  • 6.5 Experiments
  • 6.6 Conclusions
  • References
  • 7 Hashing A Face
  • 7.1 Introduction
  • 7.2 Unique Challenges of Hashing A Face
  • 7.3 Strategies for Face Hashing
  • 7.3.1 Data-Dependent Versus Data-Independent
  • 7.3.2 Linear Versus Pivots-Based Hashing
  • 7.3.3 Unsupervised Versus Supervised Hashing
  • 7.3.4 Image Versus Set/Video Hashing
  • 7.4 Face Recognition Tasks and Evaluation
  • 7.4.1 Face Verification
  • 7.4.2 Face Search
  • 7.4.3 Evaluation Metrics
  • 7.5 Face Datasets
  • 7.5.1 IJB-A: IARPA Janus Benchmark A
  • 7.5.2 IJB-B: IARPA Janus Benchmark B
  • 7.5.3 IJB-C: IARPA Janus Benchmark C
  • 7.5.4 UMD Faces
  • 7.5.5 CASIA WebFace Dataset
  • 7.6 Face Features
  • 7.6.1 UMD Features: First Generation
  • 7.6.2 UMD Features: Second Generation
  • 7.6.3 UMD Features: Third Generation
  • 7.7 Face Hashing Experiments
  • 7.7.1 Experimental Settings.
  • 7.7.2 IJB-A
  • 7.7.3 IJB-B
  • 7.7.4 IJB-C
  • 7.8 Open Issues
  • 7.9 Conclusion
  • References
  • 8 Evolution of Newborn Face Recognition
  • 8.1 Introduction
  • 8.1.1 Biometric Modalities for Newborns
  • 8.1.2 Characteristics and Challenges of Newborn Face Recognition
  • 8.2 Datasets for Newborn Face Recognition
  • 8.2.1 Newborns Face Database
  • 8.2.2 Newborns, Infants, and Toddler Longitudinal Face Database
  • 8.2.3 Children Multimodal Biometric Database (CMDB)
  • 8.3 Existing Techniques for Newborn Face Recognition
  • 8.3.1 Handcrafted Feature Extraction Methods
  • 8.3.2 Autoencoder Learning-Based Method
  • 8.3.3 Class-Based Penalty in CNN Filter Learning
  • 8.3.4 Learning Structure and Strength of CNN Filters
  • 8.4 Results and Analysis of Existing Newborn Face Recognition Techniques
  • 8.5 Conclusion
  • References
  • 9 Deep Feature Fusion for Face Analytics
  • 9.1 Introduction
  • 9.2 Feature Aggregation for Face Recognition
  • 9.2.1 Metadata-Based Feature Aggregator Network (M-FAN)
  • 9.2.2 Architecture
  • 9.2.3 Gradient Backpropagation
  • 9.2.4 Batch Training
  • 9.2.5 Experiment Setup
  • 9.2.6 Results on IJB-A
  • 9.2.7 Results on Janus CS4
  • 9.3 Feature Enhancement for Facial Action Unit Recognition
  • 9.3.1 Multi-modal Conditional Feature Enhancement (MCFE)
  • 9.3.2 Feature Extraction
  • 9.3.3 Deep Feature Enhancement
  • 9.3.4 Training MCFE for AU Recognition
  • 9.3.5 Datasets for Experimental Analysis
  • 9.3.6 Experiment Settings
  • 9.3.7 Results
  • 9.4 Conclusion
  • References
  • 10 Deep Learning for Video Face Recognition
  • 10.1 Introduction
  • 10.2 Traditional Methods
  • 10.3 Existing Deep Learning-based Approaches
  • 10.3.1 Pairwise Distance-Based Methods
  • 10.3.2 Pooling-Based Methods
  • 10.4 Neural Aggregation Network
  • 10.4.1 Feature Embedding Module
  • 10.4.2 Aggregation Module
  • 10.4.3 Network Training
  • 10.5 Experiments.
  • 10.5.1 Training Details
  • 10.5.2 Methods for Evaluation
  • 10.5.3 Results on IJB-A Dataset
  • 10.5.4 Results on YouTube Face dataset
  • 10.5.5 Results on Celebrity-1000 Dataset
  • 10.6 Conclusions
  • References
  • 11 Thermal-to-Visible Face Synthesis and Recognition
  • 11.1 The Infrared Spectrum
  • 11.2 GAN-Based Synthesis of Visible Faces From Thermal Faces
  • 11.2.1 Generative Adversarial Networks (GANs)
  • 11.2.2 GAN-Based Synthesis Network
  • 11.3 Synthesis of High-Quality Visible Faces From Polarimetric Thermal Faces Using GANs
  • 11.4 Thermal-to-Visible Face Verification Via Attribute-Preserved Synthesis
  • 11.5 Self-attention Guided Synthesis
  • 11.6 Conclusion
  • References
  • 12 Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction
  • 12.1 Introduction
  • 12.2 Background and Related Works
  • 12.3 Attacking Face Detectors
  • 12.3.1 White-Box Adversarial Perturbation Generation
  • 12.3.2 Gray-Box Adversarial Perturbation Generation
  • 12.3.3 Black-Box Adversarial Perturbation Generation
  • 12.4 Attacking Facial Landmark Extractors
  • 12.5 Experiments
  • 12.5.1 Attacking Face Detection
  • 12.5.2 Attacking Landmark Extractors
  • 12.6 Conclusion
  • References
  • 13 Multi-channel Face Presentation Attack Detection Using Deep Learning
  • 13.1 Introduction
  • 13.2 Related Work
  • 13.2.1 Feature-Based Approaches for Face PAD
  • 13.2.2 CNN-Based Approaches for Face PAD
  • 13.2.3 One-Class Models for Face PAD
  • 13.2.4 Multi-channel-Based Approaches for Face PAD
  • 13.2.5 Challenges in PAD
  • 13.3 Proposed Method
  • 13.3.1 Preprocessing
  • 13.3.2 Network Architecture
  • 13.3.3 One-Class Contrastive Loss (OCCL)
  • 13.3.4 Implementation Details
  • 13.4 Experiments
  • 13.4.1 WMCA Dataset
  • 13.4.2 MLFP Dataset
  • 13.4.3 SiW-M Dataset
  • 13.4.4 Evaluation Metrics
  • 13.4.5 Baselines.
  • 13.4.6 Experiments and Results in WMCA Dataset
  • 13.4.7 Experiments and Results in MLFP Dataset
  • 13.4.8 Experiments and Results in SiW-M Dataset
  • 13.4.9 Cross-Database Evaluations
  • 13.5 Discussions
  • 13.6 Conclusions
  • References
  • 14 Scalable Person Re-identification: Beyond Supervised Approaches
  • 14.1 Introduction
  • 14.2 Related Work
  • 14.3 Optimal Subset Selection for Labeling
  • 14.3.1 Problem Statement
  • 14.3.2 Solution Overview
  • 14.3.3 Sample Experimental Results
  • 14.4 On-Boarding New Cameras through Transfer Learning
  • 14.4.1 Problem Statement
  • 14.4.2 Solution Overview
  • 14.4.3 Sample Experimental Results
  • 14.5 Conclusions
  • References
  • 15 Towards Causal Benchmarking of Bias in Face Analysis Algorithms
  • 15.1 Introduction
  • 15.2 Related Work
  • 15.3 Face Attribute Annotation in Synthetic Images
  • 15.4 Method
  • 15.4.1 Transects: A Walk in Face Space
  • 15.4.2 Analyses Using Transects
  • 15.4.3 Human Annotation
  • 15.5 Experiments
  • 15.5.1 Gender Classifiers
  • 15.5.2 Transect Data
  • 15.5.3 Comparison of Transects to Real Face Datasets
  • 15.5.4 Analysis of Bias
  • 15.5.5 Regression Analysis
  • 15.6 Discussion and Conclusions
  • 15.6.1 Summary
  • 15.6.2 Limitations and Future Work
  • References
  • 16 Strategies of Face Recognition by Humans and Machines
  • 16.1 Introduction
  • 16.2 Identification Accuracy: Human Face Recognition
  • 16.2.1 Face Identification Performance of Untrained Humans
  • 16.2.2 Performance of Trained Versus Untrained Humans
  • 16.3 Identification Accuracy: Machines Versus Humans
  • 16.4 Fusion
  • 16.4.1 Crowd-Sourcing Methods
  • 16.4.2 Fusion: Human Participants
  • 16.4.3 Fusion: Humans and Machines
  • 16.5 Strategic Differences: Forensic Facial Examiners Versus Untrained Humans
  • 16.6 Other-Race Effects in Humans and Machines
  • 16.6.1 Theories of the Other-Race Effect.
  • 16.6.2 Other-Race Effect in Machines.

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