Face Processing: Advanced Modeling and Methods
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.
This book 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.
*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
Why Read This Book
You should read this book if you need a research-level survey of face-processing methods — it collects algorithms and modeling approaches (2D and 3D) from leading researchers and links theory to practical recognition challenges. It helps you understand the tradeoffs between appearance-based, model-based and statistical approaches and points to practical issues such as pose, illumination, and evaluation.
Who Will Benefit
Researchers and engineers in computer vision, biometrics, and signal processing who want a deeper, research-oriented overview of face modeling and recognition methods.
Level: Advanced — Prerequisites: Linear algebra, probability/statistics, basic image processing and machine learning (familiarity with PCA/LDA and optimization is helpful).
Key Takeaways
- Understand the major paradigms for face processing, including appearance-based, feature-based and model-based (2D/3D) methods.
- Apply statistical techniques such as PCA/Eigenfaces, LDA/Fisherfaces and related subspace methods to face recognition tasks.
- Analyze and mitigate practical issues like pose variation, illumination changes, expression and occlusion in face images.
- Use active appearance/shape models and 3D reconstruction approaches for pose- and expression-invariant recognition.
- Compare evaluation protocols and datasets and design experiments to benchmark face recognition algorithms.
- Integrate learning-based (including neural-network) approaches with classic signal-processing feature extraction for improved recognition.
Topics Covered
- 1. Introduction: scope and challenges in face processing
- 2. Human and computational aspects of face perception
- 3. Face detection and localization
- 4. Appearance-based methods: PCA, Eigenfaces, and subspace techniques
- 5. Discriminative methods: LDA, Fisherfaces and metric learning
- 6. Feature-based and local descriptors for faces
- 7. Active Shape and Active Appearance Models
- 8. 3D face modeling and reconstruction
- 9. Pose and illumination normalization and invariant representations
- 10. Expression, occlusion and robustness techniques
- 11. Machine learning and neural-network approaches to face recognition
- 12. Databases, evaluation protocols and performance assessment
- 13. Applications, system integration and future directions
Languages, Platforms & Tools
How It Compares
Similar in purpose to the "Handbook of Face Recognition" (Li & Jain) as an edited survey; this volume places more emphasis on modeling (2D/3D) and algorithmic treatments from contributors around 2005–2006.
