REVIEWS, REFINEMENTS AND NEW IDEAS IN FACE RECOGNITION Edited by Peter M. Corcoran Reviews, Refinements and New Ideas in Face Recognition 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 hfng, 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 Reviews, Refinements and New Ideas in Face Recognition, Edited by Peter M. Corcoran p. cm. ISBN 978-953-307-368-2 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Part 1 Statistical Face Models & Classifiers 1 Chapter 1 A Review of Hidden Markov Models in Face Recognition 3 Claudia Iancu and Peter M. Corcoran Chapter 2 GMM vs SVM for Face Recognition and Face Verification 29 Jesus Olivares-Mercado, Gualberto Aguilar-Torres, Karina Toscano-Medina, Mariko Nakano-Miyatake and Hector Perez-Meana Chapter 3 New Principles in Algorithm Design for Problems of Face Recognition 49 Vitaliy Tayanov Chapter 4 A MANOVA of LBP Features for Face Recognition 75 Yuchun Fang, Jie Luo, Gong Cheng, Ying Tan and Wang Dai Part 2 Face Recognition with Infrared Imaging 93 Chapter 5 Recent Advances on Face Recognition Using Thermal Infrared Images 95 César San Martin, Roberto Carrillo, Pablo Meza, Heydi Mendez-Vazquez, Yenisel Plasencia, Edel García-Reyes and Gabriel Hermosilla Chapter 6 Thermal Infrared Face Recognition – a Biometric Identification Technique for Robust Security System 113 Mrinal Kanti Bhowmik, Kankan Saha, Sharmistha Majumder, Goutam Majumder, Ashim Saha, Aniruddha Nath Sarma, Debotosh Bhattacharjee, Dipak Kumar Basu and Mita Nasipuri VI Contents Part 3 Refinements of Classical Methods 139 Chapter 7 Dimensionality Reduction Techniques for Face Recognition 141 Shylaja S S, K N Balasubramanya Murthy and S Natarajan Chapter 8 Face and Automatic Target Recognition Based on Super-Resolved Discriminant Subspace 167 Widhyakorn Asdornwised Chapter 9 Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition 181 Miloš Oravec, Jarmila Pavlovičová, Ján Mazanec, Ľuboš Omelina, Matej Féder and Jozef Ban Chapter 10 Constructing Kernel Machines in the Empirical Kernel Feature Space 207 Huilin Xiong and Zhongli Jiang Part 4 Robust Facial Localization & Recognition 223 Chapter 11 Additive Noise Robustness of Phase-Input Joint Transform Correlators in Face Recognition 225 Alin Cristian Teusdea and Gianina Adela Gabor Chapter 12 Robust Face Detection through Eyes Localization using Dynamic Time Warping Algorithm 249 Somaya Adwan Part 5 Face Recognition in Video 271 Chapter 13 Video-Based Face Recognition Using Spatio-Temporal Representations 273 John See, Chikkannan Eswaran and Mohammad Faizal Ahmad Fauzi Chapter 14 Real-Time Multi-Face Recognition and Tracking Techniques Used for the Interaction between Humans and Robots 293 Chin-Shyurng Fahn and Chih-Hsin Wang Part 6 Perceptual Face Recognition in Humans 315 Chapter 15 Face Recognition without Identification 317 Anne M. Cleary Preface As a baby one of our earliest stimuli is that of human faces. We rapidly learn to identify, 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 differing 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 on Statistical Face Models and Classifiers presents some reviews and refinements of well-known statistical models. The second section presents two articles exploring the use of Infrared imaging techniques to refine and even replace conventional imaging. After this follows the section with a few articles devoted to refinements of classical methods. Articles that examine new approaches to improve the robustness of several face analysis techniques are followed by two articles dealing with the challenges of real-time analysis for facial recognition in video sequences. A final article explores human perceptual issues of face recognition. I hope that you find these articles interesting, and that you learn from them and perhaps even adopts 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 [...]... overlapping rectangular windows, extracting the observation vectors and computing the probability of 2 http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html 8 Reviews, Refinements and New Ideas in Face Recognition data inside each window given the face model, using Viterbi algorithm The windows that have face model likelihood higher than a threshold are selected as possible face locations The face. .. image with sliding window (b) Construction of 1D observation vector from zigzag scanning of the sliding window [Kohir & Desai, 1998] The performance of this system is tested using the ORL database Half of the images were used in the training phase and the other half for testing (5 faces for training and the remaining 5 for testing), sampling windows of 8 × 8 and 16 × 16, were used with 50% and A Review... to face recognition, most of this work has not refined the underlying methods, but has instead combined known HMM techniques with other face analysis techniques Some work is worth 24 Reviews, Refinements and New Ideas in Face Recognition mentioning, such as that of [Le & Li, 2004] who combined a one-dimensional discrete hidden Markov model (1D-DHMM) with new way of extracting observations and using... 26 Reviews, Refinements and New Ideas in Face Recognition Chien, J-T & Liao, C-P (2008) Maximum Confidence Hidden Markov Modeling for Face Recognition Pattern Analysis and Machine Intelligence, IEEE Transactions on , Vol 30, No 4, pp 606-616, April 2008 Corcoran, P.M & Iancu, C (2011) Automatic Face Recognition System for Hidden Markov Model Techniques, Face Recognition Volume 2, Intech Publishing,... milestone in continuous speech recognition problems [Juang & Rabiner, 2005] The mathematical sophistication of HMMs combined with their successful application to a wide range of speech processing problems has prompted researchers in pattern recognition to consider their use in other areas, such as character recognition, keyword spotting, lip- 4 Reviews, Refinements and New Ideas in Face Recognition reading,... variations in facial appearance in a 12 Reviews, Refinements and New Ideas in Face Recognition robust manner Thus in this next section more challenging face recognition applications are described and further HMM approaches are considered from the literature Specifically, in this section we consider hybrid approaches based on HMMs used successfully in more challenging applications of face recognition. .. applied and the first 10 coefficients are retained, representing the observation vector Two 6 http://www.frvt.org/feret/default.htm 20 Reviews, Refinements and New Ideas in Face Recognition sets of experiments were performed, for continuous and discrete outputs For the case of continuous output, the experiments used 8 × 8 and 16 × 16 scanning windows, and 4 × 4 to 7 × 7 state structures for the P2D HMM Initially... containing frontal facial images with limited side movements and head tilt The database was comprised of 40 subjects with 10 pictures per subject The experiments used 5 images 1 http://htk.eng.cam.ac.uk/ 6 Reviews, Refinements and New Ideas in Face Recognition per person for training and the remaining 5 images for testing The results were reported as error rates, calculated as the proportion of incorrectly... images were used for training and the first six for testing and the resulting recognition rate was 97.1% A fourth test consisted of training with only two non-occluded images and testing with all the remaining images A lower recognition rate of 72% was obtained Finally, the system was trained with all 12 images for each person, and tested with the video sequences, achieving a 93.5% recognition rate 4.2... refined their research contribution To evaluate the recognition performances of the system, 2 new experiments are performed: • In a first experiment the proposed method is tested with different numbers of training and testing faces per subject The tests were performed on the ORL database, and the number of training faces was increased from 1 to 6, while the remaining faces were used in the testing . REVIEWS, REFINEMENTS AND NEW IDEAS IN FACE RECOGNITION Edited by Peter M. Corcoran Reviews, Refinements and New Ideas in Face Recognition Edited by Peter M. Corcoran. orders@intechweb.org Reviews, Refinements and New Ideas in Face Recognition, Edited by Peter M. Corcoran p. cm. ISBN 978-953-307-368-2 free online editions of InTech Books and Journals. use in other areas, such as character recognition, keyword spotting, lip- Reviews, Refinements and New Ideas in Face Recognition 4 reading, gesture and action recognition, bioinformatics and