State of the Art in Face Recognition State of the Art in Face Recognition Edited by Dr. Mario I. Chacon M. I-Tech IV Published by In-Teh In-Teh is Croatian branch of I-Tech Education and Publishing KG, Vienna, Austria. Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2009 In-teh www.in-teh.org Additional copies can be obtained from: publication@ars-journal.com First published January 2009 Printed in Croatia p. cm. ISBN 978-3-902613-42-4 1. State of the Art in Face Recognition, Dr. Mario I. Chacon M. Preface Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state. Also, the purpose of the book is to present the reader with cutting edge research on the face recognition field. Besides, the book includes recent research works from different world research groups, providing a rich diversity of approaches to the face recognition problem. This book consists of 12 chapters. The material covered in these chapters presents new advances on computer vision and pattern recognition approaches, as well as new knowledge and perspectives from different fields like, psychology and neuroscience. The chapters are organized into three groups according to their main topic. The first group focuses on classification, feature spaces, and subspaces for face recognition, Chapters 1 to 5. The second group addresses the no trivial techniques of face recognition based on holographic, 3D methods and low resolution video, covered in Chapters 6 to 9. Chapters 10 to 12 cover the third group related to human visual perception aspects on face recognition. Chapter 1 describes the achievement and perspective trends related to nearest feature classification for face recognition. The authors explain the family of nearest feature classifiers and their modified and extended versions. Among other points they provide a discussion on alternatives of the nearest feature classifiers, indicating which issues are still susceptible to be improved. The authors describe three approaches for generalizing dissimilarity representations, and they include their proposal for generalizing them by using feature lines and feature planes. Chapter 2 addresses recent subspace methods for face recognition including: singularity, regularization, and robustness. They start dealing with the singularity problem, and the authors propose a fast feature extraction technique, Bi-Directional PCA plus LDA (BDPCA+LDA), which performs LDA in the BDPCA subspace. Then, the authors presents an alternative to alleviate the over-fitting to the training set, proposing a post-processing approach on discriminant vectors, and theoretically demonstrates its relationship with the image Euclidean distance method (IMED). Finally, the authors describe an iteratively reweighted fitting of the Eigenfaces method (IRF-Eigenfaces), which first defines a generalized objective function and then uses the iteratively reweighted least-squares (IRLS) VI fitting algorithm to extract the feature vector by minimizing the generalized objective function. A multi-stage classifier for face recognition undertaken by coarse-to-fine strategy is covered in Chapter 3. The chapter includes a brief description of the DCT and PCA feature extraction methods, as well as the proposed coarse to fine stages, OAA, OAO, and multi- stage classifiers. In Chapter 4, the authors propose a method to improve the face image quality by using photometric normalization techniques. This technique based on Histogram Equalization and Homomorphic Filtering normalizes the illumination variation of the face image. The face recognition system is based on ANN with features extracted with the PCA method. The aim of Chapter 5 is to demonstrate the following points: how the feature extraction part is evolved by IPCA and Chunk IPCA, how both feature extraction part and classifier are learned incrementally on an ongoing basis, how an adaptive face recognition system is constructed and how it is effective. The chapter also explains two classifiers based on ANN, the Resource Allocating Network (RAN) and its variant model called RAN-LTM. Chapter 6 introduces a faster face recognition system based on a holographic optical disc system named FARCO 2.0. The concept of the optical parallel correlation system for facial recognition and the dedicated algorithm are described in the chapter. The chapter presents a faster correlation engine for face, image and video data using optical correlation, and an online face recognition system based on phase information. The first 3D technique for face recognition is covered in Chapter 7. The authors describe a 3D face mesh modeling for 3D face recognition. The purpose of the authors is to show a model-based paradigm that represents the 3D facial data of an individual by a deformed 3D mesh model useful for face recognition application. Continuing with 3D methods, the occlusion problem in face recognition system is handled in Chapter 8. In this chapter the authors describe their approach, a full automatic recognition pipeline based on 3D imaging. They take advantage of the 3D data to solve the occlusion problem because it has depth information available. Chapter 9 presents a model-based approach for simultaneous tracking and increasing super-resolution of known object types in low resolution video. The approach is also based on a 3D mask. The 3D mask allows estimating translation and rotation parameters between two frames which is equivalent to calculating a dense sub-pixel accurate optical flow field and subsequent warping into a reference coordinate system. The material covered in Chapter 10 is aimed to show how joint knowledge from human face recognition and unsupervised systems may provide a robust alternative compared with other approaches. The chapter includes a detailed description of how low resolution features can be combined with an unsupervised ANN for face recognition. Chapter 11 addresses the issue of gender classification by information fusion of hair and face. Unlike most face recognition systems, the proposed method in this chapter considers the important role of hair features in gender classification. The chapter presents a study of hair feature extraction and the combination of hair classifier and face classifier. The authors show that the key point of classifier fusion is to determine how classifiers interact with each other. The fusion information method used is based on the fuzzy integral. Last but not at least, a challenging issue on face recognition is faced in Chapter 12, emotion modeling and facial affect recognition in human-computer and human-robot interaction. In this chapter the authors present a review of prevalent psychology theories on VII emotion with the purpose to disambiguate their terminology and identify the fitting computational models that can allow affective interactions in the desired environments. It is our interest, editors and chapter authors that this book contributes to a fast and deep development on the challenging filed of face recognition systems. We also expect the reader really finds this book both helpful and promising. January 2009 Editor Dr. Mario I. Chacon M. Chihuahua Institute of Technology, Mexico Contents Preface V 1. Trends in Nearest Feature Classification for Face Recognition – Achievements and Perspectives 001 Mauricio Orozco-Alzate and César Germán Castellanos-Domínguez 2. Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness 025 Wangmeng Zuo, Kuanquan Wang and Hongzhi Zhang 3. A Multi-Stage Classifier for Face Recognition Undertaken by Coarse-to-fine Strategy 051 Jiann-Der Lee and Chen-Hui Kuo 4. PCA-ANN Face Recognition System based on Photometric Normalization Techniques 071 Shahrin Azuan Nazeer and Marzuki Khalid 5. Online Incremental Face Recognition System Using Eigenface Feature and Neural Classifier 087 Seiichi Ozawa, Shigeo Abe, Shaoning Pang and Nikola Kasabov 6. High Speed Holographic Optical Correlator for Face Recognition 109 Eriko Watanabe and Kashiko Kodate 7. 3D Face Mesh Modeling for 3D Face Recognition 131 Ansari A-Nasser, Mahoor Mohammad and Abdel-Mottaleb Mohamed 8. Occlusions in Face Recognition: a 3D Approach 151 Alessandro Colombo, Claudio Cusano and Raimondo Schettini 9. A Model-based Approach for Combined Tracking and Resolution Enhancement of Faces in Low Resolution Video 173 Annika Kuhl, Tele Tan and Svetha Venkatesh X 10. Face Recognition Based on Human Visual Perception Theories and Unsupervised ANN 195 Mario I. Chacon M. and Pablo Rivas P. 11. Gender Classification by Information Fusion of Hair and Face 215 Zheng Ji, Xiao-Chen Lian and Bao-Liang Lu 12. Emotion Modelling and Facial Affect Recognition in Human-Computer and Human-Robot Interaction 231 Lori Malatesta, John Murray, Amaryllis Raouzaiou, Antoine Hiolle, Lola Cañamero and Kostas Karpouzis [...]... projection point in the interpolating part, projection point in the backward extrapolating part and projection point in the forward extrapolating part 10 State of the Art in Face Recognition Afterwards, the rectification procedure is achieved in order to avoid the effect of the interpolation inaccuracy It consists in removing feature line segments trespassing the territory of another class To do so, the concept... Segmentation consists in cutting off the feature line in order to preserve only the ~ interpolating part which is called a feature line segment Lcij Segmentation is aimed to avoid the extrapolation inaccuracy When the orthogonal projection of a query point onto Lcij is in ~ ~ the interpolating part; that is, in Lcij , the distance of such query point to Lcij is computed in c the same way that the distance... the SSS problem without using linear dimensionality reduction methods In fact, the dimensionality is increased since the number of feature lines depends combinatorially on the number of training points or objects per class Soon later, a number of studies for improving the concept of feature lines were reported A family of extensions of the nearest feature line classifier appeared, mainly encompassing... τ=(x-xci)(xcj-xci)/║xcj-xci║2 Parameter τ is called the position parameter When 0 . extrapolating part. State of the Art in Face Recognition 10 Afterwards, the rectification procedure is achieved in order to avoid the effect of the interpolation inaccuracy. It consists in removing. normalization of the Euclidean or Manhattan distance from a query point to each training point by the shortest distance between the corresponding training point to training points of a different. State of the Art in Face Recognition State of the Art in Face Recognition Edited by Dr. Mario I. Chacon M. I-Tech IV Published by In- Teh In- Teh