Edizione	[1st ed. 2021.]
Pubbl/distr/stampa	Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica	1 online resource (VI, 407 p. 182 illus., 169 illus. in color.)
Disciplina	006.37
Collana	Advances in Computer Vision and Pattern Recognition
Soggetto topico	Human face recognition (Computer science) Machine learning
ISBN	3-030-74697-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	1. Deep CNN Face Recognition: Looking at the Past and the Future -- 2. Face Segmentation, Face Swapping, and Their Effect on Face Recognition -- 3. Disentangled Representation Learning and its Application to Face Analytics -- 4. Learning 3D Face Morphable Model from In-the-wild Images -- 5. Deblurring Face Images using Deep Networks -- 6. Blind-Superresolution of Faces for Surveillance -- 7. Hashing a Face.
Record Nr.	UNINA-9910495352903321

Pubbl/distr/stampa	Cambridge, Mass., : MIT Press, c2011
Descrizione fisica	1 online resource (299 p.)
Disciplina	006.3/7
Altri autori (Persone)	CurioCristóbal <1972-> BülthoffHeinrich H GieseMartin A PoggioTomaso
Soggetto topico	Human face recognition (Computer science)
Soggetto genere / forma	Electronic books.
ISBN	1-282-97839-X 9786612978395 0-262-28931-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover; Contents; Foreword; Introduction; I Psychophysics; 1 Is Dynamic Face Perception Primary?; 2 Memory for Moving Faces; 3 Investigating the Dynamic Characteristics Important for Face Recognition; 4 Recognition of Dynamic Facial Action Probed by Visual Adaptation; 5 Facial Motion and Facial Form; 6 Dynamic Facial Speech; II Physiology; 7 Dynamic Facial Signaling; 8 Engaging Neocortical Networks with Dynamic Faces; 9 Multimodal Studies Using Dynamic Faces; 10 Perception of Dynamic Facial Expressions and Gaze; 11 Moving and Being Moved; III Computation; 12 Analyzing Dynamic Faces 13 Elements for a Neural Theory of the Processing of Dynamic Faces14 Insights on Spontaneous Facial Expressions from Automatic Expression Measurement; 15 Real-Time Dissociation of Facial Appearance and Dynamics during Natural Conversation; 16 Markerless Tracking of Dynamic 3D Scans of Faces; Contributors; Index
Record Nr.	UNINA-9910459816103321

Pubbl/distr/stampa	Cambridge, Mass., : MIT Press, ©2011
Descrizione fisica	1 online resource (299 p.)
Disciplina	006.3/7
Altri autori (Persone)	CurioCristóbal <1972-> BülthoffHeinrich H GieseMartin A
Soggetto topico	Human face recognition (Computer science)
Soggetto non controllato	NEUROSCIENCE/Visual Neuroscience NEUROSCIENCE/General
ISBN	1-282-97839-X 9786612978395 0-262-28931-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover; Contents; Foreword; Introduction; I Psychophysics; 1 Is Dynamic Face Perception Primary?; 2 Memory for Moving Faces; 3 Investigating the Dynamic Characteristics Important for Face Recognition; 4 Recognition of Dynamic Facial Action Probed by Visual Adaptation; 5 Facial Motion and Facial Form; 6 Dynamic Facial Speech; II Physiology; 7 Dynamic Facial Signaling; 8 Engaging Neocortical Networks with Dynamic Faces; 9 Multimodal Studies Using Dynamic Faces; 10 Perception of Dynamic Facial Expressions and Gaze; 11 Moving and Being Moved; III Computation; 12 Analyzing Dynamic Faces 13 Elements for a Neural Theory of the Processing of Dynamic Faces14 Insights on Spontaneous Facial Expressions from Automatic Expression Measurement; 15 Real-Time Dissociation of Facial Appearance and Dynamics during Natural Conversation; 16 Markerless Tracking of Dynamic 3D Scans of Faces; Contributors; Index
Record Nr.	UNINA-9910789837203321

Autore	Belmonte Romain
Pubbl/distr/stampa	Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022]
Descrizione fisica	1 online resource (312 pages)
Disciplina	006.42
Soggetto topico	Human face recognition (Computer science) Image processing
ISBN	1-394-17385-7 1-394-17383-0
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	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.
Record Nr.	UNINA-9910643860303321

Pubbl/distr/stampa	Amsterdam ; ; Boston, : Elsevier / Academic Press, c2006
Descrizione fisica	1 online resource (755 p.)
Disciplina	006.37 006.37 22
Altri autori (Persone)	ZhaoWenyi ChellappaRama
Soggetto topico	Human face recognition (Computer science) Biometric identification - Research
Soggetto genere / forma	Electronic books.
ISBN	1-281-05319-8 9786611053192 0-08-048884-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Front Cover; FACE PROCESSING: Advanced Modeling and Methods; Copyright Page; Contents; Contributors; Preface; PART I: THE BASICS; Chapter 1. A Guided Tour of Face Processing; Chapter 2. Eigenfaces and Beyond; Chapter 3. Introduction to the Statistical Evaluation of Face-Recognition Algorithms; PART II: FACE MODELING; COMPUTATIONAL ASPECTS; Chapter 4. 3D Morphable Face Model, a Unified Approach for Analysis and Synthesis of Images; Chapter 5. Expression-Invariant Three-Dimensional Face Recognition; Chapter 6. 3D Face Modeling From Monocular Video Sequences Chapter 7. Face Modeling by Information MaximizationPSYCHOPHYSICAL ASPECTS; Chapter 8. Face Recognition by Humans; Chapter 9. Predicting Human Performance for Face Recognition; Chapter 10. Spatial Distribution of Face and Object Representations in the Human Brain; PART III: ADVANCED METHODS; Chapter 11. On the Effect of Illumination and Face Recognition; Chapter 12. Modeling Illumination Variation with Spherical Harmonics; Chapter 13. A Multisubregion-Based Probabilistic Approach Toward Pose-Invariant Face Recognition Chapter 14. Morphable Models for Training a Component-Based Face-Recognition SystemChapter 15. Model-Based Face Modeling and TrackingWith Application to Videoconferencing; Chapter 16. A survey of 3D and Multimodal 3D+2D Face Recognition; Chapter 17. Beyond One Still Image: Face Recognition from Multiple Still Images or Video Sequence; Chapter 18. Subset Modeling of Face Localization Error, Occlusion, and Expression; Chapter 19. Near Real-time Robust Face and Facial-Feature Detection with Information-Based Maximum Discrimination Chapter 20. Current Landscape of Thermal Infrared Face RecognitionChapter 21. Multimodal Biometrics: Augmenting Face With Other Cues; Index
Record Nr.	UNINA-9910458493703321