NEW APPROACHES TO CHARACTERIZATION AND RECOGNITION OF FACES Edited by Peter M. Corcoran New Approaches to Characterization and Recognition of Faces Edited by Peter M. Corcoran Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original 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. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Mirna Cvijic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright Leolintang 2010. Used under license from Shutterstock.com First published July, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org New Approaches to Characterization and Recognition of Faces, Edited by Peter M. Corcoran p. cm. ISBN 978-953-307-515-0 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Part 1 Architectures and Coding Techniques 1 Chapter 1 Automatic Face Recognition System for Hidden Markov Model Techniques 3 Peter M. Corcoran and Claudia Iancu Chapter 2 Large-Scale Face Image Retrieval: A Wyner-Ziv Coding Approach 29 Jean-Paul Kouma and Haibo Li Part 2 3D Methods for Face Recognition 45 Chapter 3 3D Face Recognition 47 Naser Zaeri Chapter 4 Face Image Synthesis and Interpretation Using 3D Illumination-Based AAM Models 69 Salvador E. Ayala-Raggi, Leopoldo Altamirano-Robles and Janeth Cruz-Enriquez Chapter 5 Processing and Recognising Faces in 3D Images 93 Eyad Elyan and Daniel C Doolan Part 3 Video and Real-Time Techniques 113 Chapter 6 Real-Time Video Face Recognition for Embedded Devices 115 Gabriel Costache, Sathish Mangapuram, Alexandru Drimbarean, Petronel Bigioi and Peter Corcoran Chapter 7 Video Based Face Recognition Using Convolutional Neural Network 131 Shefa A. Dawwd and Basil Sh. Mahmood VI Contents Chapter 8 Adaptive Fitness Approach - an Application for Video-Based Face Recognition 153 Alaa Eleyan, Hüseyin Özkaramanli and Hasan Demirel Chapter 9 Real Time Robust Embedded Face Detection Using High Level Description 171 Khalil Khattab, Philippe Brunet, Julien Dubois and Johel Miteran Part 4 Methods of Face Characterization and Feature Detection 195 Chapter 10 Face Discrimination Using the Orientation and Size Recognition Characteristics of the Spreading Associative Neural Network 197 Kiyomi Nakamura and Hironobu Takano Chapter 11 The Methodology for Facial Features Detection 213 Jacek Naruniec Chapter 12 Exploring and Understanding the High Dimensional and Sparse Image Face Space: a Self-Organized Manifold Mapping 225 Edson C. Kitani, Emilio M. Hernandez, Gilson A. Giraldi and Carlos E. Thomaz Part 5 Perceptual Aspects of Face Recognition 239 Chapter 13 The Effects of Right/Left Temporal Lobe Lesions on the Recognition of Familiar Faces 241 Guido Gainotti, Monica Ferraccioli and Camillo Marra Preface As a baby one of our earliest stimuli is that of human faces. We rapidly learn to identi- fy, characterize and eventually distinguish those who are near and dear to us. This skill stays with us throughout our lives. As humans, face recognition is an ability we accept as commonplace. It is only when we attempt to duplicate this skill in a computing system that we begin to realize the complexity of the underlying problem. Understandably, there are a multitude of dif- fering approaches to solving this complex problem. And while much progress has been made many challenges remain. This book is arranged around a number of clustered themes covering different aspects of face recognition. The first section presents an architecture for face recognition based on Hidden Markov Models and is followed by an article on coding methods for image retrieval in large databases. The second section of this book is devoted to 3 articles on 3D methods of face recognition and is followed by a section with 5 articles covering various aspects and techniques of face recognition in video sequences and in real-time. This is followed by a section devoted to characterization and the detection of features in faces. The complexity of facial features and expressions is often simplified or disre- garded by face recognition methodologies. Finally an article on the human perception of faces and how different neurological or psychological disorders can affect this. I hope that you find these articles interesting, and that you learn from them and per- haps even adopt some of these methods for use in your own research activities. Sincerely, Peter M. Corcoran Vice-Dean, College of Engineering & Informatics, National University of Ireland Galway (NUIG), Galway, Ireland [...]... architecture of a face recognition system 4 New Approaches to Characterization and Recognition of Faces 1 Face detection and cropping block: this is the first stage of any face recognition system and the key difference between a semi-automatic and a fully automatic face recognizer In order to make the recognition system fully automatic, the detection and extraction of faces from an image should also be automatic... application of HMM techniques to the face recognition problem implies the use of an inherently 1D method of pattern matching to solve an inherently 2D problem So why did researchers think this might work? Well, as the most significant facial features of a frontal face image occur in a natural order, from top to bottom, and this sequence is 10 New Approaches to Characterization and Recognition of Faces immutable,... above, and the observations probability distributions for the top-level HMM, that is: the probabilities of each horizontal row of observations given each Fig 6 Doubly embedded Viterbi 14 New Approaches to Characterization and Recognition of Faces superstate Now Viterbi is applied on the top-level HMM and the optimal sequence of superstates is obtained given the sequence of rows of observation vectors... 16 New Approaches to Characterization and Recognition of Faces in the given images These are then cropped and saved on disk as new JPG image files Note that this process facilitates a manual inspection or supplemental testing of a set of images to determine if they are correctly and uniformly cropped To achieve the functionality of this program, there are two principal stages: i the declaration of the... affect recognition rate 6 New Approaches to Characterization and Recognition of Faces There are many techniques that can be used to enlarge or reduce the size of an image These methods generally realize a trade-off between speed and the degree to which they reduce the occurrence of visual artifacts in the resulting image The most commonly used resize method is called bicubic interpolation and has... detection and cropping As mentioned in the previous section, face detection is one of the most important steps in a face recognition system and differentiates between semi-automatic and fully automatic face recognizer The goal of an automatic face detector is to search for human faces in a still image and, if found, to accurately return their locations In order to make the detection fully automatic the... Discussion of our experiments Throughout this chapter we described a series of tests performed with the purpose of finding the optimal combination of factors that influence the recognition process: size of face image, topology of the model, that is number of superstates, number of states for each super state and number of Gaussians to model the observation vectors, illumination normalization technique to diminish... observations can be provided: the best recognition rates are obtained when combining HE and CLAHE regardless of the order 24 - New Approaches to Characterization and Recognition of Faces Better recognition rates are achieved using the simple HE technique when compared to the more sophisticated CLAHE and LogDCT techniques LogDCT returns very poor results even when compared to the rates obtained when no illumination... are allowed ( a1,ij > 0) and which are k not ( a1,ij =0); in our left -to- right HMM the only transitions allowed are selftransitions and transitions to the next state, so the probability of transition to k previous states is 0 For a numerical example we choose N 0 5 , N 1 3,6,6,6, 3 where k = 1, 2, , 5, and K = 3 12 New Approaches to Characterization and Recognition of Faces Step 2 Uniform segmentation:... center, and a more accurate model will match the number of Gaussians to the number of primary clusters The objective of this experiment is to test how a varying number of Gaussians will influence the underlying recognition rates We used face regions of size 128 × 128 pixels This size of images should provide close to an optimal recognition rate as detailed in section 8.1 19 Automatic Face Recognition . NEW APPROACHES TO CHARACTERIZATION AND RECOGNITION OF FACES Edited by Peter M. Corcoran New Approaches to Characterization and Recognition of Faces Edited. architecture of a face recognition system New Approaches to Characterization and Recognition of Faces 4 1. Face detection and cropping block: this is the first stage of any face recognition. significant facial features of a frontal face image occur in a natural order, from top to bottom, and this sequence is New Approaches to Characterization and Recognition of Faces 10 immutable,