Face analysis under uncontrolled conditions : from face detection to expression recognition / Romain Belmonte and Benjamin Allaert.

Format:

Book

Author/Creator:

Belmonte, Romain, author.

Allaert, Benjamin, author.

Series:

Sciences. Image. Information seeking in images and videos

Language:

English

Subjects (All):

Human face recognition (Computer science).

Physical Description:

1 online resource (312 pages)

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., [2022]

Summary:

Face analysis is essential for a large number of applications such as human-computer interaction or multimedia (e.g. content indexing and retrieval). Although many approaches are under investigation, performance under uncontrolled conditions is still not satisfactory. The variations that impact facial appearance (e.g. pose, expression, illumination, occlusion, motion blur) make it a difficult problem to solve. This book describes the progress towards this goal, from a core building block - landmark detection - to the higher level of micro and macro expression recognition. Specifically, the book addresses the modeling of temporal information to coincide with the dynamic nature of the face. It also includes a benchmark of recent solutions along with details about the acquisition of a dataset for such tasks.

Contents:

Cover

Title Page

Copyright Page

Contents

Preface

Part 1. Facial Landmark Detection

Introduction to Part 1

Chapter 1. Facial Landmark Detection

1.1. Facial landmark detection in still images

1.1.1. Generative approaches

1.1.2. Discriminative approaches

1.1.3. Deep learning approaches

1.1.4. Handling challenges

1.1.5. Summary

1.2. Extending facial landmark detection to videos

1.2.1. Tracking by detection

1.2.2. Box, landmark and pose tracking

1.2.3. Adaptive approaches

1.2.4. Joint approaches

1.2.5. Temporal constrained approaches

1.2.6. Summary

1.3. Discussion

1.4. References

Chapter 2. Effectiveness of Facial Landmark Detection

2.1. Overview

2.2. Datasets and evaluation metrics

2.2.1. Image and video datasets

2.2.2. Face preprocessing and data augmentation

2.2.3. Evaluation metrics

2.2.4. Summary

2.3. Image and video benchmarks

2.3.1. Compiled results on 300W

2.3.2. Compiled results on 300VW

2.4. Cross-dataset benchmark

2.4.1. Evaluation protocol

2.4.2. Comparison of selected approaches

2.5. Discussion

2.6. References

Chapter 3. Facial Landmark Detection with Spatio-temporal Modeling

3.1. Overview

3.2. Spatio-temporal modeling review

3.2.1. Hand-crafted approaches

3.2.2. Deep learning approaches

3.2.3. Summary

3.3. Architecture design

3.3.1. Coordinate regression networks

3.3.2. Heatmap regression networks

3.4. Experiments

3.4.1. Datasets and evaluation protocols

3.4.2. Implementation details

3.4.3. Evaluation on SNaP-2DFe

3.4.4. Evaluation on 300VW

3.4.5. Comparison with existing models

3.4.6. Qualitative results

3.4.7. Properties of the networks

3.5. Design investigations

3.5.1. Encoder-decoder

3.5.2. Complementarity between spatial and temporal information.

3.5.3. Complementarity between local and global motion

3.6. Discussion

3.7. References

Conclusion to Part 1

Part 2. Facial Expression Analysis

Introduction to Part 2

Chapter 4. Extraction of Facial Features

4.1. Introduction

4.2. Face detection

4.2.1. Point-of-interest detection algorithms

4.2.2. Face alignment approaches

4.2.3. Synthesis

4.3. Face normalization

4.3.1. Dealing with head pose variations

4.3.2. Dealing with facial occlusions

4.3.3. Synthesis

4.4. Extraction of visual features

4.4.1. Facial appearance features

4.4.2. Facial geometric features

4.4.3. Facial dynamics features

4.4.4. Facial segmentation models

4.4.5. Synthesis

4.5. Learning methods

4.5.1. Classification versus regression

4.5.2. Fusion model

4.5.3. Synthesis

4.6. Conclusion

4.7. References

Chapter 5. Facial Expression Modeling

5.1. Introduction

5.2. Modeling of the affective state

5.2.1. Categorical modeling

5.2.2. Dimensional modeling

5.2.3. Synthesis

5.3. The challenges of facial expression recognition

5.3.1. The variation of the intensity of the expressions

5.3.2. Variation of facial movement

5.3.3. Synthesis

5.4. The learning databases

5.4.1. Improvement of learning data

5.4.2. Comparison of learning databases

5.4.3. Synthesis

5.5. Invariance to facial expression intensities

5.5.1. Macro-expression

5.5.2. Micro-expression

5.5.3. Synthesis

5.6. Invariance to facial movements

5.6.1. Pose variations (PV) and large displacements (LD)

5.6.2. Synthesis

5.7. Conclusion

5.8. References

Chapter 6. Facial Motion Characteristics

6.1. Introduction

6.2. Characteristics of the facial movement

6.2.1. Local constraint of magnitude and direction

6.2.2. Local constraint of the motion distribution.

6.2.3. Motion propagation constraint

6.3. LMP

6.3.1. Local consistency of the movement

6.3.2. Consistency of local distribution

6.3.3. Coherence in the propagation of the movement

6.4. Conclusion

6.5. References

Chapter 7. Micro- and Macro-Expression Analysis

7.1. Introduction

7.2. Definition of a facial segmentation model

7.3. Feature vector construction

7.3.1. Motion features vector

7.3.2. Geometric features vector

7.3.3. Features fusion

7.4. Recognition process

7.5. Evaluation on micro- and macro-expressions

7.5.1. Learning databases

7.5.2. Micro-expression recognition

7.5.3. Macro-expressions recognition

7.5.4. Synthesis of experiments on micro- and macro-expressions

7.6. Same expression with different intensities

7.6.1. Data preparation

7.6.2. Fractional time analysis

7.6.3. Analysis on a different time frame

7.6.4. Synthesis of experiments on activation segments

7.7. Conclusion

7.8. References

Chapter 8. Towards Adaptation to Head Pose Variations

8.1. Introduction

8.2. Learning database challenges

8.3. Innovative acquisition system (SNaP-2DFe)

8.4. Evaluation of face normalization methods

8.4.1. Does the normalization preserve the facial geometry?

8.4.2. Does normalization preserve facial expressions?

8.5. Conclusion

8.6. References

Conclusion to Part 2

List of Authors

Index

EULA.

Notes:

Description based on print version record.

Other Format:

Print version: Belmonte, Romain Face Analysis under Uncontrolled Conditions

ISBN:

9781394173853

1394173857

9781394173839

1394173830

OCLC:

1347124012