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The Resource Advances in Face Detection and Facial Image Analysis

Advances in Face Detection and Facial Image Analysis

Label
Advances in Face Detection and Facial Image Analysis
Title
Advances in Face Detection and Facial Image Analysis
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Contributor
Subject
Language
eng
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Advances in Face Detection and Facial Image Analysis
Label
Advances in Face Detection and Facial Image Analysis
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4470798
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Contents -- A Deep Learning Approach to Joint Face Detection and Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Image Segmentation -- 2.2 Face Detection -- 3 Deep Learning Approach to Face Detection and Facial Segmentation -- 3.1 Deep Learning Framework -- 3.2 Our Proposed Approach -- 3.2.1 One-Class Support Vector Machines (OCSVM) -- 3.2.2 Post-processing Steps -- 4 Our Experimental Results -- 5 Conclusions -- References -- Face Detection Coupling Texture, Color and Depth Data -- 1 Introduction -- 2 The Proposed Approach -- 2.1 Depth Map Alignment and Segmentation -- 2.2 Image Size Filter -- 2.3 Flatness\Unevenness Filter -- 2.4 Segmentation Based Filtering -- 2.5 Eye Based Filtering -- 2.6 Filtering Based on the Analysis of the Depth Values -- 3 Experimental Results -- 4 Conclusion -- References -- Lighting Estimation and Adjustment for Facial Images -- 1 Introduction -- 2 Approximation of the Illumination Cone -- 3 Statistical Model of Basis Images -- 4 Estimating the Basis Images -- 4.1 Estimating Lighting Coefficients -- 4.2 Estimating the Error Term -- 4.3 Recovering the Basis Images -- 4.4 Recognition -- 5 Experiments on Lighting Estimation -- 5.1 Recovered Basis Images -- 5.2 Recognition -- 5.3 Multiple Lighting Sources -- 6 Perception-Based Lighting Model -- 7 Image Sequence Lighting Adjustment -- 7.1 Single Image Enhancement -- 7.2 Lighting Adjustment of Successive Images -- 8 Experiments on Lighting Adjustment -- 9 Conclusion -- References -- Advances, Challenges, and Opportunities in Automatic Facial Expression Recognition -- 1 Facial Expression Theory: Three Models -- 2 The Standard Algorithmic Pipeline -- 3 Challenges -- 4 Opportunities -- 5 Conclusions -- References -- Exaggeration Quantified: An Intensity-Based Analysis of Posed Facial Expressions -- 1 Introduction and Related Work -- 2 Proposed Method
  • 2.1 Stage 1: Face Expression Recognition -- 2.1.1 Training -- 2.1.2 Testing -- 2.2 Stage 1 Landmark Selection -- 2.2.1 Stage 1 Geometric Descriptors -- 2.2.2 Stage 1 Appearance Descriptors -- 2.2.3 Stage 1 Classification -- 2.3 Stage 2 Classification: Posed Expression Recognition -- 2.3.1 Training -- 2.3.2 Testing -- 2.3.3 Stage 2 Geometric Descriptors -- 2.3.4 Stage 2 Appearance Descriptors -- 2.3.5 Integration of Descriptors -- 2.3.6 Stage 2 Classification -- 3 Results and Discussion -- 3.1 Dataset Description -- 3.2 Performance Evaluation -- 3.3 Baseline Methods -- 3.4 Experimental Analysis -- 3.4.1 Stage 1 Evaluation -- 3.4.2 Stage 2 Evaluation -- 4 Conclusions -- References -- Method of Modelling Facial Action Units Using Partial Differential Equations -- 1 Introduction -- 2 The PDE Method for Parametric Surfaces -- 2.1 Solution of the PDE -- 3 Method of Facial Modelling -- 3.1 Boundary Curve Extraction -- 4 Modeling Action Units Using PDE Formulation -- 5 Results and Analysis -- 6 Conclusions -- References -- Trends in Machine and Human Face Recognition -- 1 Introduction -- 2 Machine Face Recognition: Its Existing Challenges and Emerging Methods -- 2.1 Challenges for Face Recognition -- 2.1.1 Illumination Variation -- 2.1.2 Pose and Viewpoint Variation -- 2.1.3 Expression Variation -- 2.1.4 Aging -- 2.1.5 Scale Variation -- 2.1.6 Occlusions -- 2.1.7 Motion Blur -- 2.2 Pre-processing and Normalization -- 2.3 Trends in Unconstrained Face Recognition: Promising Directions -- 2.3.1 Methods Using Invariance Identity-Preserving Transformations -- 2.3.2 Video Based Face Recognition -- 2.3.3 Deep-Learning Framework for Face Recognition -- 3 Evaluation and Benchmark Competitions -- 3.1 FRVT 2013 Findings and Conclusions -- 3.2 Emerging Databases -- 3.2.1 The Good Bad and the Ugly (GBU) Datasets -- 3.2.2 FR in Mobile Environment
  • 3.2.3 T̀̀he Famous'' Labeled Faces in the Wild, Its Old and New Protocols -- 3.2.4 YouTube Video Database -- 3.3 Summary of the Emerging Databases -- 4 Human Recognition of Faces -- 4.1 Temporal Cues That Aid Face Recognition: Two Hypotheses to Explain Motion Advantage -- 4.2 How Do Human Beings Handle the Big 4? -- 4.2.1 Face Recognition Across Different Illumination Conditions -- 4.2.2 Face Recognition Across Different Facial Expressions -- 4.2.3 Face Recognition Across Different Viewing Perspectives -- 4.2.4 Face Recognition Across Age Differences -- 4.3 Transcending the Big Four: Evaluating Human Performance in Dynamic Perspective Invariant Face Recognition -- 5 Summary and Future Trends -- References -- Labeled Faces in the Wild: A Survey -- 1 Introduction -- 1.1 Verification and Identification -- 1.2 Background -- 1.3 Variations on Traditional Supervised Learning and the Relationship to Face Recognition -- 1.3.1 Faces in the Wild and Labeled Faces in the Wild -- 2 Algorithms and Methods -- 2.1 The LFW Protocols -- 2.1.1 Why Study Restricted Data Protocols? -- 2.1.2 Order of Discussion -- 2.2 Unrestricted with Labeled Outside Data -- 2.2.1 Attribute and Simile Classifiers for Face Verification, 2009 kumar2009attribute -- 2.2.2 Face Recognition with Learning-Based Descriptor, 2010 cao2010face -- 2.2.3 An Associate-Predict Model for Face Recognition, 2011 yin2011associate -- 2.2.4 Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition, 2011 taigman2011leveraging -- 2.2.5 Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification, 2012 berg2012tom -- 2.2.6 Bayesian Face Revisited: A Joint Formulation, 2012 chen2012bayesian -- 2.2.7 Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification, 2013 chen2013blessing
  • 2.2.8 A Practical Transfer Learning Algorithm for Face Verification, 2013 cao2013practical -- 2.2.9 Hybrid Deep Learning for Face Verification, 2013 sun2013hybrid -- 2.2.10 POOF: Part-Based One-vs-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation, 2013 berg2013poof -- 2.2.11 Learning Discriminant Face Descriptor for Face Recognition, 2014 lei2014learning -- 2.2.12 Face++, 2014 -- 2.2.13 DeepFace: Closing the Gap to Human-Level Performance in Face Verification, 2014 taigman2014deepface -- 2.2.14 Recover Canonical-View Faces in the Wild with Deep Neural Networks, 2014 zhu2014recover -- 2.2.15 Deep Learning Face Representation from Predicting 10,000 Classes, 2014 sun2014deep -- 2.2.16 Surpassing Human-Level Face Verification Performance on LFW with GaussianFace, 2014 lu2014surpassing -- 2.2.17 Deep Learning Face Representation by Joint Identification-Verification, 2014 sun2014deepid2 -- 2.2.18 Deeply Learned Face Representations are Sparse, Selective and Robust, 2014 sun2014deeply -- 2.2.19 DeepID3: Face Recognition with Very Deep Neural Networks, 2015 sun2015deepid3 -- 2.2.20 FaceNet: A Unified Embedding for Face Recognition and Clustering, 2015 schroff2015facenet -- 2.2.21 Tencent-BestImage, 2015 tencent -- 2.3 Label-Free Outside Data Protocols -- 2.3.1 Face Recognition Using Boosted Local Features, 2003 MERLface -- 2.3.2 LFW Results Using a Combined Nowak Plus MERL Recognizer, 2008 huang2008lfw -- 2.3.3 Is That You? Metric Learning Approaches for Face Identification, 2009 guillaumin2009you -- 2.3.4 Multiple One-Shots for Utilizing Class Label Information, 2009 taigman2009multiple -- 2.3.5 Attribute and Simile Classifiers for Face Verification, 2009 kumar2009attribute -- 2.3.6 Similarity Scores Based on Background Samples, 2010 wolf2010similarity
  • 2.3.7 Rectified Linear Units Improve Restricted Boltzmann Machines, 2010 nair2010rectified -- 2.3.8 Face Recognition with Learning-based Descriptor, 2010 cao2010face -- 2.3.9 Cosine Similarity Metric Learning for Face Verification, 2011 nguyen2011cosine -- 2.3.10 Beyond Simple Features: A Large-Scale Feature Search Approach to Unconstrained Face Recognition, 2011 cox2011beyond -- 2.3.11 Face Verification Using the LARK Representation, 2011 seo2011face -- 2.3.12 Probabilistic Models for Inference About Identity, 2012 li2012probabilistic -- 2.3.13 Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval, 2012 huang2012large -- 2.3.14 Distance Metric Learning with Eigenvalue Optimization, 2012 ying2012distance -- 2.3.15 Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks, 2012 huang2012learning -- 2.3.16 Bayesian Face Revisited: A Joint Formulation, 2012 chen2012bayesian -- 2.3.17 Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification, 2013 chen2013blessing -- 2.3.18 Fisher Vector Faces in the Wild, 2013 simonyan2013fisher -- 2.3.19 Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild, 2013 cui2013fusing -- 2.3.20 Towards Pose Robust Face Recognition, 2013 yi2013towards -- 2.3.21 Similarity Metric Learning for Face Recognition, 2013 cao2013similarity -- 2.3.22 Fast High Dimensional Vector Multiplication Face Recognition, 2013 barkan2013fast -- 2.3.23 Discriminative Deep Metric Learning for Face Verification in the Wild, 2014 hu2014discriminative -- 2.3.24 Large Margin Multi-Metric Learning for Face and Kinship Verification in the Wild, 2014 hu2014large
  • 2.3.25 Effective Face Frontalization in Unconstrained Images, 2014 hassner2014effective
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1 online resource (438 pages)
Form of item
online
Isbn
9783319259581
Media category
computer
Media MARC source
rdamedia
Media type code
c
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