Ramakrishnan, S.

English

IntechOpen

Selection of our books indexed in the Book Citation Index in Web of Science™ Core Collection (BKCI)Interested in publishing with us? Contact book.department@intechopen.comNumbers displayed above are based on latest data collected. For more information visit www.intechopen.comOpen access books availableCountries delivered to Contributors from top 500 universitiesInternational  authors and editorsOur authors are among themost cited scientistsDownloadsWe are IntechOpen,the world’s leading publisher ofOpen Access booksBuilt by scientists, for scientists12.2%122,000 135MTOP 1%1544,800Chapter 1Introductory Chapter: Face Recognition - Overview,Dimensionality Reduction, and Evaluation MethodsS. RamakrishnanAdditional information is available at the end of the chapterhttp://dx.doi.org/10.5772/63995Face recognition is one of most popular and powerful applications in modern computingindustries [1–4]. It has found applications ranging from person identification (surveillance) [5]to emotion identification (human–machine interaction) [6]. Over the past few decades,researchers in field of computers and electrical and electronics engineering have workedcontinuously to improve the performances of the face recognition systems. In‐spite of thesecontinuous efforts, there are still a plenty of scope for the new and additional research in thefield of face recognition. This is due to the popularization of light‐weight computing devices,increased customer expectations, and business competitions.Now the world best cameras in terms of resolution are available in smart phones at affordableprice, and CCD cameras are found even in houses and almost in all commercial, business, andoffice environments including small‐sized enterprises. Amount of face images being capturedkeep on increasing, and recognition of faces among these huge databases makes the taskfurther challenging. One of most the important subtopics in face recognition is dimensionalityreduction [1], because storing and processing of these high‐resolution face images from hugedatabase using light‐weight devices require dimensionality reduction.Several different face recognition systems, including hardware (cameras, memory disk, andprocessors) and software, are available in the market. These face recognition systems providebetter performance in one aspect and lack in other aspect. Comprehensive evaluation theperformances of face recognition systems is the need of the hour.Keeping these factors in mind, this book on “face recognition” is focusing on dimensionalityreduction and evaluation methods. This book is brief but comprehensive. Other than thisintroductory chapter, this book has four more chapters, two chapters for dimensionalityreduction and one for an overview of the face recognition systems and evaluation methods.© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,and reproduction in any medium, provided the original work is properly cited.Rest of this introductory chapter is spared for providing a brief outlines, linkages, importance,and significances of the four chapters of this book.Chapter 1: This chapter provides an overview of face recognition, various issues in facerecognition, and different methods of face recognition and applications of face recognition.We strongly encourage the young readers to thoroughly study this chapter to get the bird'seye view of face recognition. Advanced readers can proceed to Chapter 2 directly.Typical complex engineering applications requires various submodules and proper fine tuningof all those modules to make the application perfect. Face recognition, one of the toughestcomplex engineering applications, certainly requires number of submodules. A few importantsubmodules are pre-processing, face detection and normalization, feature database andclassifier. These building blocks are presented in Chapter 1 in a simple way. Various challengesin face recognition include [7–10]: scale invariance, rotation invariance, translation invariance,illumination invariance, and emotion invariance. All these make the task difficult for the facerecognition system. These challenges are discussed in Chapter 1.Authors of Chapter 1 presented a comprehensive overview of various classical face recognitionmethods. Classification of 18 different classical face recognition algorithms based on local andholistic features is also presented in this chapter nicely. Over and above, the classical facerecognition methods and modern face recognition methods are briefly introduced in Chapter1. Modern techniques include artificial neural networks, wavelets‐based methods, descriptor‐based method, 3D methods, and video‐based techniques. Advantages and disadvantages ofboth classical and modern methods are narrated in Chapter 1. This will help the students tochoose an appropriate technique for doing their projects. Eight different potential applicationsof face recognition systems are highlighted in Chapter 1. Ideally, through reading of Chapter1 will be of immense help for the young readers.Chapter 2: Traditional pattern recognition methods can either be a supervised learning orunsupervised learning. Face recognition methods comes under supervised learning methods.Supervised learning requires proper and complete labeling of all patterns and objects. Due tosocial media and in general internet, the amount of face images being generated is steeplyincreasing. Most of these face images are not labeled by required for the face recognition systemto provide satisfactory performance. Hence, a new type of learning method, which is a subtypeof supervised leaning called, semi‐supervised learning method is being applied to modern facerecognition methods [11–16]. This chapter is dealing with this new learning method and alsoaddressing dimensionality reduction concept in semi‐supervised learning.Semi‐supervised learning methods can be grouped under transductive learning or inductionlearning. Authors of this chapter have systematically presented the state‐of‐the‐art methodsand nicely introduced their contribution in this chapter. Authors of Chapter 2 have proposeda new and effective algorithm for semi‐supervised dimensionality using local and globalregression. The algorithm proposed in this chapter is capable of reducing dimensions of bothtransductive learning and induction learning. Proposed algorithm is explained from the firstprinciples so that the readers with pattern recognition or image processing background caneasily understand and apply this in their projects. Presentation of the proposed algorithm isFace Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods2excellent as it has proper mix of analytical and descriptive treatments. Theorems employed bythe authors are also provided and over and above the proposed concepts are illustrated withintermediate results. This is a must read subsection for the young learners.In addition to the smooth and neat presentation of the proposal and related works, the authorsof Chapter 2 have conducted extensive experiments and beautifully presented the results alongwith appropriate discussions. Experiments are conducted not only using synthetic dataset butalso using three of the real‐world bench mark datasets, namely UMNIST, extended Yale B, andMIT‐CBCL. Experimental results are also compared with existing methods. This chapter iswell written and much useful for the both young and senior researchers working in patternrecognition.Chapter 3: Among the various challenges of a typical pattern recognition system, dimension‐ality reduction is one of important tasks. Image processing applications such as face recogni‐tion should focus on dimensionality reduction for better performance. Subspace projectiontechniques are highly useful and classical option in face recognition is useful for reducing thedimension. Principle component analysis (PCA) and linear discriminant analysis (LDA) areboth popular and powerful subspace projection techniques over the past few decades [17] andapplied in almost all pattern recognition systems [18–26].In face recognition, input–output pairs are known as it is mostly supervised. Here, linearregression that used to fit a linear function to a set of input–output pairs is latest techniqueand also comes under subspace projection. Chapter 3 is focusing on latest technique namedlinear regression and its variants, over and above the classical subspace projection techniques.Important and critical issues in face recognition, namely partial occlusion, illuminationvariance, different expression, pose variance, and low resolution are all addressed andpresented.This chapter is self‐contained and comprehensive. Authors of this chapter have provided abrief overview of how face images are represented and recognized. Two of the classicalsubspace projection methods, namely PCA and LDA are briefly presented in this chapter. Thisquick introduction will help even advanced readers to recall the basics. In addition to thissimilarity metrics used in the classifier stage of face recognition systems are also presented.Various latest subspace optimization techniques, namely linear regression classification,robust linear regression classification, improved principal component regression, unitaryregression classification, linear discriminant regression classification, generalized linearregression classification, and trimmed linear regression are all presented. These eight methodsare discussed in this chapter with correct blend of mathematical equations and theoreticaldescriptions.Authors have conducted extensive experiments are presented the results. Performanceanalysis is carried out on the benchmark datasets, namely Yale B, AR, FERT, and FEI. Com‐parative analysis of the various subspace projection methods and linear regression and itsvariants are also provided precisely in this chapter. This chapter is self‐reliant and will be usefulto both young and advanced readers.Introductory Chapter: Face Recognition - Overview, Dimensionality Reduction, and Evaluation Methodshttp://dx.doi.org/10.5772/639953Chapter 4: Performance evaluation is one of most important aspects in face recognitionapplications [2, 3, 27–29]. Recognition rate (or classification accuracy) is the commonly usedmetrics to analyze the performance of the face recognition methods. But there are several otherimportant and critical metrics available for performance evaluation of the system. In thischapter, those metrics are presented and demonstrated. A brief outline of face recognitiontechniques and methods are provided in this chapter. Four important component of a confu‐sion matrix, namely true positive, true negative, false positive, and false negative are present‐ed. Based on these four parameters, seven significant evaluation metrics, namely precision,recall, sensitivity, specificity, fallout, error rate, and accuracy are presented in this chapter.Receiver operating characteristics (ROC) curve analysis is presented sensitivity and specificity.Salient points in ROC analysis are illustrated clearly for all possible performances of facerecognition methods.Like ROC combines sensitivity and specificity, F‐score combines precision and recall, and thismetric is better explained in this chapter. In addition to these metrics, the following metricsare also briefed: false match rate, false non‐match rate, equal error rate, failure to enroll rate,and failure to capture rate.Authors of this chapter have conducted experiments to analyze the performances of the facerecognition using these metrics. Three different case studies are presented using face imagesfrom the benchmark datasets. Whoever developing face recognition system finds this chapteruseful.Final word: This book has five chapters including this introductory. This book can be a briefmaterial and will be highly useful for students, researchers, and practicing engineeringworking in pattern recognition, image processing, and machine vision.Author detailsS. Ramakrishnan*Address all correspondence to: ram_f77@yahoo.comDepartment of Information Technology, Dr. Mahalingam College of Engineering andTechnology, Pollachi, Coimbatore, IndiaReferences[1] Ramakrishnan, S., and Ibrahiem M.M.EI Emary. Computational techniques andalgorithms for image processing (ISBN: 978‐3‐8433‐5802‐6) & (ISBN: 3843358028).Lambert Academic Publishing, Germany, 2012.Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods4[2] Gonzalez, Rafael C., and Richard E. Woods. Digital image processing (3rd Edition)(ISBN: 013168728X). Prentice‐Hall, Inc., Upper Saddle River, 2006.[3] Jain, Anil K., Fundamentals of digital image processing. Prentice Hall Inc., UpperSaddle River, 1989.[4] Duda, Richard O., and Peter E. Hart, Pattern classification and scene analysis, vol. 3.Wiley, New York, 1973.[5] Szeliski, Richard, Computer vision: algorithms and applications. Springer, New York,2010.[6] Ming, Yue. Robust regional bounding spherical descriptor for 3D face recognition andemotion analysis. Image and Vision Computing, 35:14–22, 2015.[7] Ding, Changxing, Chang Xu, and Dacheng Tao. Multi‐task pose‐invariant face recog‐nition. IEEE Transactions on Image Processing, 24(3):980–993, 2015.[8] Yang, Allen Y., et al. Fast‐minimization algorithms for robust face recognition. IEEETransactions on Image Processing, 22(8):3234–3246, 2013.[9] Liao, Shengcai, Anubhav K. Jain, and Stan Z. Li. Partial face recognition: alignment‐free approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5):1193–1205, 2013.[10] Jun, Bongjin, and Daijin Kim. Robust face detection using local gradient patterns andevidence accumulation. Pattern Recognition, 45(9):3304–3316, 2012.[11] Yu, Jun, Meng Wang, and Dacheng Tao. Semisupervised multiview distance metriclearning for cartoon synthesis. IEEE Transactions on Image Processing, 21(11):4636–4648,2012.[12] Criminisi, Antonio, Jamie Shotton, and Ender Konukoglu. Decision forests: a unifiedframework for classification, regression, density estimation, manifold learning andsemi‐supervised learning. Foundations and Trends® in Computer Graphics and Vision, 7(2–3):81–227, 2012.[13] Gao, Quanxue, et al. A novel semi‐supervised learning for face recognition. Neurocom‐puting, 152:69–76, 2015.[14] Yi, Yugen, et al. Semi‐supervised local ridge regression for local matching based facerecognition. Neurocomputing, 167:132–146, 2015.[15] Yang, Shuyuan, et al. Semisupervised dual‐geometric subspace projection for dimen‐sionality reduction of hyperspectral image data. IEEE Transactions on Geoscience andRemote Sensing, 52(6):3587–3593, 2014.[16] Zhao, Mingbo, et al. Learning from normalized local and global discriminativeinformation for semi‐supervised regression and dimensionality reduction. InformationSciences, 324:286–309, 2015.Introductory Chapter: Face Recognition - Overview, Dimensionality Reduction, and Evaluation Methodshttp://dx.doi.org/10.5772/639955[17] Imran Naseem, Roberto Togneri and Mohammed Bennamoun, Linear regression forface recognition, IEEE Transactions on PAMI, 32(11):2106–2112, 2010.[18] Zhang, Yong‐Qin, et al. Guided image filtering using signal subspace projection. ImageProcessing, IET, 7(3):270–279, 2013.[19] Pyatykh, Stanislav, Jurgen Hesser, and Lei Zheng. Image noise level estimation byprincipal component analysis. IEEE Transactions on Image Processing, 22(2):687–699,2013.[20] Wan, Tao, Chenchen Zhu, and Zengchang Qin. Multifocus image fusion based onrobust principal component analysis. Pattern Recognition Letters, 34(9):1001–1008, 2013.[21] Moulin, Christophe, et al. Fisher linear discriminant analysis for text‐image combina‐tion in multimedia information retrieval. Pattern Recognition, 47(1):260–269, 2014.[22] Shu, Xin, Yao Gao, and Hongtao Lu. Efficient linear discriminant analysis with localitypreserving for face recognition. Pattern Recognition, 45(5):892–1898, 2012.[23] Huang, Shih‐Ming, and Jar‐Ferr Yang. Linear discriminant regression classification forface recognition. IEEE Transactions on Signal Processing Letters, 20(1):91–94, 2013.[24] Sharma, Alok, and Kuldip K. Paliwal. A two‐stage linear discriminant analysis for face‐recognition. Pattern Recognition Letters, 33(9):1157–1162, 2012.[25] Huang, Shih‐Ming, and Jar‐Ferr Yang. Improved principal component regression forface recognition under illumination variations. IEEE Transactions on Signal ProcessingLetters, 19(4):179–182, 2012.[26] Naseem, Imran, Roberto Togneri, and Mohammed Bennamoun. Robust regression forface recognition. Pattern Recognition, 45(1):104–118, 2012.[27] Yang, Meng, et al. Gabor feature based robust representation and classification for facerecognition with Gabor occlusion dictionary. Pattern Recognition, 46(7):1865–1878, 2013.[28] Huang, Shih‐Ming, and Jar‐Ferr Yang. Unitary regression classification with totalminimum projection error for face recognition. IEEE Transactions on Signal ProcessingLetters, 20(5):443–446, 2013.[29] Galbally, Javier, Sébastien Marcel, and Julian Fierrez. Image quality assessment for fakebiometric detection: application to iris, fingerprint, and face recognition. 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