Face analysis under uncontrolled conditions : from face detection to expression recognition

Face analysis under uncontrolled conditions from face detection to expression recognition

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 faci...

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Detalles Bibliográficos
Otros Autores: Belmonte, Romain, author (author), Allaert, Benjamin, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc [2022]
Colección:Sciences. Image. Information seeking in images and videos
Materias:
Human face recognition (Computer science)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009724224106719
Tabla de Contenidos:
  • 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.

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