Face Recognition

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Face Recognition

Face Recognition

Edited by

Milo Oravec

In-Tech

intechweb.org

Published by In-Teh In-Teh Olajnica 19/2, 32000 Vukovar, Croatia 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. 2010 In-teh www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published April 2010 Printed in India Technical Editor: Zeljko Debeljuh Cover designed by Dino Smrekar Face Recognition, Edited by Milo Oravec p. cm. ISBN 978-953-307-060-5

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PrefaceFace recognition has been studied for many years in the context of biometrics. The human face belongs to the most common biometrics, since humans recognize faces throughout their whole lives; at the same time face recognition is not intrusive. Face recognition systems show many advantages, among others easy implementation, easy cooperation with other biometric systems, availability of face databases. Nowadays, automatic methods of face recognition in ideal conditions (for two-dimensional face images) are generally considered to be solved. This is confirmed by many recognition results and reports from tests running on standard large face databases. Nevertheless, the design of a face recognition system is still a complex task which requires thorough choice and proposal of preprocessing, feature extraction and classification methods. Many tasks are still to be solved, e.g. face recognition in an unconstrained and uncontrolled environment (varying pose, illumination and expression, a cluttered background, occlusion), recognition of non-frontal facial images, the role of the face in multimodal biometric systems, real-time operation, one sample problem, 3D recognition, face recognition in video; that is why many researchers study face biometric extensively. This book aims to bring together selected recent advances, applications and original results in the area of biometric face recognition. They can be useful for researchers, engineers, graduate and postgraduate students, experts in this area and hopefully also for people interested generally in computer science, security, machine learning and artificial intelligence. Various methods, approaches and algorithms for recognition of human faces are used by authors of the chapters of this book, e.g. PCA, LDA, artificial neural networks, wavelets, curvelets, kernel methods, Gabor filters, active appearance models, 2D and 3D representations, optical correlation, hidden Markov models and others. Also a broad range of problems is covered: feature extraction and dimensionality reduction (chapters 1-4), 2D face recognition from the point of view of full system proposal (chapters 5-10), illumination and pose problems (chapters 11-13), eye movement (chapter 14), 3D face recognition (chapters 15-19) and hardware issues (chapters 19-20). Chapter 1 reviews the most relevant feature extraction techniques (both holistic and local feature) used in 2D face recognition and also introduces a new feature extraction technique. Chapter 2 presents the n-dimensional extension of PCA, which solves numerical difficulties and provides near optimal linear classification property. Chapter 3 is devoted to curvelets; authors concentrate on fast digital curvelet transform. In chapter 4, a dimensionality reduction method based on random projection is proposed and compressive classification algorithms that are robust to random projection dimensionality reduction are reviewed.

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In chapter 5, the author presents a modular system for face recognition including a method that can suppress unwanted features and make useful decisions on similarity irrespective of the complex nature of the underlying data. Chapter 6 presents discussion of appearancebased methods vs. local description methods and the proposal of a novel face recognition system based on the use of interest point detectors and local descriptors. Chapter 7 focuses on wavelet-based face recognition schemes and presents their performance using a number of benchmark databases of face images and videos. Chapter 8 presents a complex view on the proposal of a biometric face recognition system including methodology, settings of parameters and the influence of input image quality on face recognition accuracy. In chapter 9, authors propose a face recognition system built as a cascade connection of an artificial neural network and pseudo 2D hidden Markov models. In chapter 10, an experimental evaluation of the performance of VG-RAM weightless neural networks for face recognition using well-known face databases is presented. Chapter 11 addresses the problem of illumination in face recognition including mathematical illumination modeling, influence of illumination on recognition results and the current state-of-art of illumination processing and its future trends. Chapter 12 brings the proposal of a novel face representation based on phase responses of the Gabor filter bank which is characterized by its robustness to illumination changes. Chapter 13 presents illumination and pose-invariant face alignment based on an active appearance model. Chapter 14 reviews current literature about eye movements in face recognition and provides answers to several questions relevant to this topic. Chapter 15 gives an overview of surface representations for 3D face recognition; also surface representations promising in terms of future research that have not yet been reported in current face recognition literature are discussed. Chapter 16 presents framework for 3D face and expression recognition taking into account the fact that the deformation of the face surface is always related to different expressions. Chapter 17 addresses security leakages and privacy protection issues in biometric systems and presents latest results of template protection techniques in 3D face recognition systems. Chapter 18 presents a 3D face recognition system based on pseudo 2D hidden Markov models using an expression-invariant representation of faces. Chapter 19 covers some of the latest developments in optical correlation techniques for face recognition using the concept of spectral fusion; also a new concept of correlation filter called segmented composite filter is employed that is suitable for 3D face recognition. Chapter 20 presents an implementation of the Neocognitron neural network using a highperformance computing architecture based on a graphics processing unit. The editor owes special thanks to authors of all included chapters for their valuable work. April 2010 Slovak University of Technology Faculty of Electrical Engineering and Information Technology Department of Applied Informatics and Information Technology Ilkoviova 3, 812 19 Bratislava, Slovak Republic e-mail: milos.oravec@stuba.sk

Milo Oravec

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ContentsPreface 1. FeatureExtractionandRepresentationforFaceRecognitionM.SaquibSarfraz,OlafHellwichandZahidRiaz

V 001 021 035 047 065 087 099

2. AnExtensionofPrincipalComponentAnalysisHongchuanYuandJianJ.Zhang

3. CurveletBasedFeatureExtractionTanayaGuhaandQ.M.JonathanWu

4. COMPRESSIVECLASSIFICATIONFORFACERECOGNITIONAngshulMajumdarandRababK.Ward

5. Pixel-LevelDecisionsbasedRobustFaceImageRecognitionAlexPappachenJames

6. Interest-PointbasedFaceRecognitionSystemCesarFernandezandMariaAsuncionVicente

7. WaveletBasedFaceRecognitionSchemesSabahA.Jassim

8. FaceRecognitioninIdealandNoisyConditions UsingSupportVectorMachines,PCAandLDAMiloOravec,JnMazanec,JarmilaPavloviov,PavelEibenandFedorLehocki

125

9. Pseudo2DHiddenMarkovModeland NeuralNetworkCoefficientsinFaceRecognitionDomenicoDaleno,LuciaCariello,MarcoGianniniandGiuseppeMastronardi

151 171

10. VG-RAMWeightlessNeuralNetworksforFaceRecognitionAlbertoF.DeSouza,ClaudineBadue,FelipePedroni,StivenSchwanzDias, HallyssonOliveiraandSoterioFerreiradeSouza

11. IlluminationProcessinginFaceRecognitionYongpingLi,ChaoWangandXinyuAo

187

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12. FromGaborMagnitudetoGaborPhaseFeatures: TacklingtheProblemofFaceRecognitionunderSevereIlluminationChangesVitomirtrucandNikolaPavei

215 239 255 273 295 315 329

13. RobustFaceAlignmentforIlluminationandPoseInvariantFaceRecognitionFatihKahraman,BinnurKurt,MuhittinGkmen

14. EyeMovementsinFaceRecognitionJanetH.Hsiao

15. Surfacerepresentationsfor3DfacerecognitionThomasFabry,DirkSmeetsandDirkVandermeulen

16. AnIntegrativeApproachtoFaceandExpressionRecognitionfrom3DScansChao Li

17. TemplateProtectionFor3DFaceRecognitionXuebingZhou,ArjanKuijperandChristophBusch

18. GeodesicDistancesandHiddenMarkovModelsforthe3DFaceRecognitionGiuseppeMastronardi,LuciaCariello,DomenicoDalenoandMarcelloCastellano

19. UnderstandingCorrelationTechniquesfor FaceRecognition:FromBasicstoApplicationsA.AlfalouandC.Brosseau

353

20. ParallelFaceRecognitionProcessingusingNeocognitron NeuralNetworkandGPUwithCUDAHighPerformanceArchitectureGustavoPoliandJosHirokiSaito

381

FeatureExtractionandRepresentationforFaceRecognition

1

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Feature Extraction and Representation for Face Recognition1Computer

Vision Research Group, Department of Electrical Engineering