Major strides have been made in face processing in the last ten years due to the fast growing need for security in various locations around the globe. A human eye can discern the details of a specific face with relative ease. It is this level of detail that researchers are striving to create with ever evolving computer technologies that will become our perfect mechanical eyes. The difficulty that confronts researchers stems from turning a 3D object into a 2D image. That subject is covered in depth from several different perspectives in this volume.
Face Processing: Advanced Modeling and Methods begins with a comprehensive introductory chapter for those who are new to the field. A compendium of articles follows that is divided into three sections. The first covers basic aspects of face processing from human to computer. The second deals with face modeling from computational and physiological points of view. The third tackles the advanced methods, which include illumination, pose, expression, and more. Editors Zhao and Chellappa have compiled a concise and necessary text for industrial research scientists, students, and professionals working in the area of image and signal processing.
Key Features
- Contributions from over 35 leading experts in face detection, recognition and image processing
- Over 150 informative images with 16 images in FULL COLOR illustrate and offer insight into the most up-to-date advanced face processing methods and techniques
- Extensive detail makes this a need-to-own book for all involved with image and signal processing
Dedication
CONTRIBUTORS
PREFACE
PART I: THE BASICS
Chapter 1: A GUIDED TOUR OF FACE PROCESSING
1.1 INTRODUCTION TO FACE PROCESSING
1.2 FACE PERCEPTION: THE PSYCHOPHYSICS/NEUROSCIENCE ASPECT
1.3 FACE DETECTION AND FEATURE EXTRACTION
1.4 METHODS FOR FACE RECOGNITION
1.5 ADVANCED TOPICS IN FACE RECOGNITION
ACKNOWLEDGMENTS
Chapter 2: EIGENFACES AND BEYOND
2.1 INTRODUCTION
2.2 ORIGINAL CONTEXT AND MOTIVATIONS OF EIGENFACES
2.3 EIGENFACES
2.4 IMPROVEMENTS TO AND EXTENSIONS OF EIGENFACES
2.5 SUMMARY
ACKNOWLEDGMENTS
Chapter 3: INTRODUCTION TO THE STATISTICAL EVALUATION OF FACE-RECOGNITION ALGORITHMS
3.1 INTRODUCTION
3.2 FACE-IDENTIFICATION DATA, ALGORITHMS, AND PERFORMANCE MEASURES
3.3 A BERNOULLI MODEL FOR ALGORITHM TESTING
3.4 NONPARAMETRIC RESAMPLING METHODS
3.5 EXPANDING THE LDA VERSUS PCA COMPARISON
3.6 ADVANCED MODELING
3.7 CONCLUSION
Appendix A NOTATIONAL SUMMARY
Appendix B PARTICULARS FOR THE ELASTIC BUNCH GRAPH ALGORITHM
ACKNOWLEDGMENTS
PART II: FACE MODELING COMPUTATIONAL ASPECTS
Chapter 4: 3D MORPHABLE FACE MODEL, A UNIFIED APPROACH FOR ANALYSIS AND SYNTHESIS OF IMAGES
4.1 INTRODUCTION
4.2 PARAMETERS OF VARIATION IN IMAGES OF HUMAN FACES
4.3 TWO- OR THREE-DIMENSIONAL IMAGE MODELS
4.4 IMAGE ANALYSIS BY MODEL FITTING
4.5 MORPHABLE FACE MODEL
4.6 COMPARISON OF FITTING ALGORITHM
4.7 RESULTS
4.8 CONCLUSION
Chapter 5: EXPRESSION-INVARIANT THREE-DIMENSIONAL FACE RECOGNITION
5.1 INTRODUCTION
5.2 ISOMETRIC MODEL OF FACIAL EXPRESSIONS
5.3 EXPRESSION-INVARIANT REPRESENTATION
5.4 THE 3DFACE SYSTEM
5.5 RESULTS
5.6 CONCLUSIONS
ACKNOWLEDGMENTS
Chapter 6: 3D FACE MODELING FROM MONOCULAR VIDEO SEQUENCES
6.1 INTRODUCTION
6.2 SFM-BASED 3D FACE MODELING
6.3 CONTOUR-BASED 3D FACE MODELING
6.4 CONCLUSIONS
Chapter 7: FACE MODELING BY INFORMATION MAXIMIZATION
7.1 INTRODUCTION
7.2 INDEPENDENT-COMPONENT ANALYSIS
7.3 IMAGE DATA
7.4 ARCHITECTURE I: STATISTICALLY INDEPENDENT BASIS IMAGES
7.5 ARCHITECTURE II: A FACTORIAL FACE CODE
7.6 EXAMINATION OF THE ICA REPRESENTATIONS
7.7 LOCAL BASIS IMAGES VERSUS FACTORIAL CODES
7.8 DISCUSSION
7.9 FACE MODELING AND INFORMATION MAXIMIZATION: A COMPUTATIONAL NEUROSCIENCE PERSPECTIVE
ACKNOWLEDGMENTS
Chapter 8: FACE RECOGNITION BY HUMANS
8.1 INTRODUCTION
8.2 WHAT ARE THE LIMITS OF HUMAN FACE RECOGNITION SKILLS?
8.3 WHAT CUES DO HUMANS USE FOR FACE-RECOGNITION?
8.4 WHAT IS THE TIMELINE OF DEVELOPMENT OF HUMAN FACE RECOGNITION SKILLS?
8.5 WHAT ARE SOME BIOLOGICALLY PLAUSIBLE STRATEGIES FOR FACE RECOGNITION?
8.6 CONCLUSION
Chapter 9: PREDICTING HUMAN PERFORMANCE FOR FACE RECOGNITION
9.1 INTRODUCTION
9.2 FACE-BASED FACTORS AND THE FACE-SPACE MODEL
9.3 VIEWING CONSTRAINTS
9.4 MOVING FACES
9.5 MOTION AND FAMILIARITY
9.6 FAMILIARITY AND EXPERIENCE
ACKNOWLEDGMENTS
Chapter 10: SPATIAL DISTRIBUTION OF FACE AND OBJECT REPRESENTATIONS IN THE HUMAN BRAIN
10.1 THE VENTRAL OBJECT-VISION PATHWAY
10.2 LOCALLY DISTRIBUTED REPRESENTATIONS OF FACES AND OBJECTS IN VENTRAL TEMPORAL CORTEX
10.3 EXTENDED DISTRIBUTION OF FACE AND OBJECT REPRESENTATIONS
10.4 SPATIALLY DISTRIBUTED FACE AND OBJECT REPRESENTATIONS
PART III: ADVANCED METHODS
Chapter 11: ON THE EFFECT OF ILLUMINATION AND FACE RECOGNITION
11.1 INTRODUCTION
11.2 NON-EXISTENCE OF ILLUMINATION INVARIANTS
11.3 THEORY AND FOUNDATIONAL RESULTS
11.4 MENAGERIE
11.5 EXPERIMENTS AND RESULTS
11.6 CONCLUSION
ACKNOWLEDGMENT
Chapter 12: MODELING ILLUMINATION VARIATION WITH SPHERICAL HARMONICS
12.1 INTRODUCTION
12.2 BACKGROUND AND PREVIOUS WORK
12.3 ANALYZING LAMBERTIAN REFLECTION USING SPHERICAL HARMONICS
12.4 APPLICATIONS OF LAMBERTIAN 9-TERM SPHERICAL-HARMONIC MODEL
12.5 SPECULARITIES: CONVOLUTION FORMULA FOR GENERAL MATERIALS
12.6 RELAXING AND BROADENING THE ASSUMPTIONS: RECENT WORK
12.7 CONCLUSION
ACKNOWLEDGMENTS
Chapter 13: A MULTISUBREGION-BASED PROBABILISTIC APPROACH TOWARD POSE-INVARIANT FACE RECOGNITION
13.1 INTRODUCTION
13.2 MODELING CHANGE OF LOCAL APPEARANCE ACROSS POSES
13.3 RECOGNITION
13.4 RECOGNITION EXPERIMENTS
13.5 CONCLUSION
Chapter 14: MORPHABLE MODELS FOR TRAINING A COMPONENT-BASED FACE-RECOGNITION SYSTEM
14.1 INTRODUCTION
14.2 MORPHABLE MODELS
14.3 FACE DETECTION AND RECOGNITION
14.4 EXPERIMENTAL RESULTS
14.5 LEARNING COMPONENTS FOR FACE RECOGNITION
14.6 SUMMARY AND OUTLOOK
Chapter 15: MODEL-BASED FACE MODELING AND TRACKING WITH APPLICATION TO VIDEOCONFERENCING
15.1 INTRODUCTION
15.2 STATE OF THE ART
15.3 FACIAL GEOMETRY REPRESENTATION
15.4 OVERVIEW OF THE 3D FACE-MODELING SYSTEM
15.5 A TOUR OF THE SYSTEM ILLUSTRATED WITH A REAL VIDEO SEQUENCE
15.6 MORE FACE-MODELING EXPERIMENTS
15.7 STEREO 3D HEAD-POSE TRACKING
15.8 APPLICATION TO EYE-GAZE CORRECTION
15.9 CONCLUSIONS
ACKNOWLEDGMENT
Chapter 16: A SURVEY OF 3D AND MULTIMODAL 3D+2D FACE RECOGNITION
16.1 INTRODUCTION
16.2 SURVEY OF 3D AND MULTIMODAL 2D+3D FACE RECOGNITION
16.3 EXAMPLE 3D AND MULTIMODAL 3D+2D FACE RECOGNITION
16.4 CHALLENGES TO IMPROVED 3D FACE RECOGNITION
ACKNOWLEDGMENTS
Chapter 17: BEYOND ONE STILL IMAGE: FACE RECOGNITION FROM MULTIPLE STILL IMAGES OR A VIDEO SEQUENCE
17.1 INTRODUCTION
17.2 BASICS OF FACE RECOGNITION
17.3 PROPERTIES
17.4 REVIEW
17.5 FUTURE
17.6 CONCLUSIONS
Chapter 18: SUBSET MODELING OF FACE LOCALIZATION ERROR, OCCLUSION, AND EXPRESSION
18.1 INTRODUCTION
18.2 MODELING THE LOCALIZATION ERROR
18.3 MODELING OCCLUSIONS AND EXPRESSION CHANGES
18.4 EXPERIMENTAL RESULTS
18.5 DISCUSSION AND FUTURE WORK
18.6 CONCLUSIONS
Acknowledgments
Chapter 19: NEAR REAL-TIME ROBUST FACE AND FACIAL-FEATURE DETECTION WITH INFORMATION-BASED MAXIMUM DISCRIMINATION
19.1 INTRODUCTION
19.2 INFORMATION-BASED MAXIMUM DISCRIMINATION
19.3 IBMD FACE AND FACIAL-FEATURE DETECTION
19.4 EXPERIMENTS AND RESULTS
19.5 CONCLUSIONS AND FUTURE WORK
Chapter 20: CURRENT LANDSCAPE OF THERMAL INFRARED FACE RECOGNITION
20.1 INTRODUCTION
20.2 PHENOMENOLOGY
20.3 SAME-SESSION RECOGNITION
20.4 TIME-LAPSE RECOGNITION
20.5 OUTDOOR RECOGNITION
20.6 RECOGNITION IN THE DARK WITH THERMAL INFRARED
20.7 CONCLUSION
ACKNOWLEDGMENT
Chapter 21: MULTIMODAL BIOMETRICS: AUGMENTING FACE WITH OTHER CUES
21.1 INTRODUCTION
21.2 DESIGN OF A MULTIMODAL BIOMETRIC SYSTEM
21.3 EXAMPLES OF MULTIMODAL BIOMETRIC SYSTEMS
21.4 CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
INDEX
