IGI Global, 2010. — 405 p.
The analysis of the human face via image (and video) is one of the most interesting and focusing research topics in the last years for the image community. From the analysis (sensing, computing, and perception) of face images, much information can be extracted, such as the sex/gender, age, facial expression, emotion/ temper, mentality/mental processes and behavior/psychology, and the health of the person captured. According to this information, many practical tasks can be performed and completed; these include not only person identification or verification (face recognition), but also the estimation and/or determination of person’s profession, hobby, name (recovered from memory), etc.
Research on face image analysis has been carried out and is being conducted around various application topics, such as (in alphabetical order) age estimation, biometrics, biomedical instrumentations, emotion assessment, face recognition, facial expression classification, gender determination, human-computer/ human-machine interaction, human behavior and emotion study, industrial automation, military service, psychosis judgment, security checking systems, social signal processing, surveillance systems, sport training, tele-medicine service, etc.
To push the deep theoretical study of this important area, to prompt the further development of face analysis techniques, and to guide the people working in image area for applying suitable methods, a comprehensive and detailed volume for the new advancements of face image analysis would be a must, and these are the overall objectives and the main mission of this book. Comprehensive coverage of various branches of face image analysis is provided by more than 30 leading experts from 16 countries and regions around the world.
In addition, new achievements and new challenges often come up together. With the accumulation of research results in face image analysis, the need for a uniform description of this area and a review of recent developments are even increasing. This book tries to set the fundamental knowledge for, and introduce the advancements in recent years in, face image analysis. The principal concern of this book is to provide those in the face image analysis community with a general methodology that allows them to integrate the research results and to apply them in various applications.
The complete coverage of this book is also reflected in the title, Advances in Face Image Analysis: Techniques and Technologies, where techniques would refer to the different methodologies and models utilized, whereas technologies would refer to the applications and tools applied. This book is intended for scientists and engineers who are engaged in the research and development of face image analysis techniques and who wish to keep their paces with the advances of this field. The objective of this collection is to review and survey new forward-thinking research and development in face image analysis technologies.
Face image analysis is a specialized field of image analysis (that is an important layer of image engineering), though face images have some particularities, the methodologies and techniques presented in this book are ready to be extended to other types of images, and are the foundation of many image analysis applications.
As various face analysis techniques have been widely used in many areas, the audience of such a book should be all persons that study/research in the disciplines such as, image and information processing, computer vision, pattern recognition. Such a book would be suitable for using in specialized courses for graduate students and to be read by researchers in companies of related domains.
Section 1 Introduction and Background.
Face, Image, and Analysis.
Face Searching in Large Databases.
Section 2 Facial Feature Extraction.
A Review of Facial Feature Detection Algorithms.
Gabor and Log-Gabor Wavelet for Face Recognition.
Efficient Face Retrieval Based on Bag of Facial Features.
Section 3 Feature Dimensionality Reduction.
Feature Selection in High Dimension.
Transform Based Feature Extraction and Dimensionality Reduction Techniques.
FastNMF: Efficient NMF Algorithm for Reducing Feature Dimension.
Section 4 Face Recognition.
Sparse Representation for View-Based Face Recognition.
Probabilistic Methods to Face Registration and Recognition.
Discriminant Learning Using Training Space Partitioning.
Section 5 Facial Expression Classification.
From the Face to Facial Expression.
Facial Expression Analysis by Machine Learning.
Subtle Facial Expression Recognition in Still Images and Videos.
Photometric Normalization Techniques for Illumination Invariance.
Pose and Illumination Invariance with Compound Image Transforms.
Configural Processing Hypothesis and Face-Inversion Effect.