LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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[15] Xiujuan Chai , Shiguang Shan, Wen Gao. Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation ICICSPCM 2003 1518 December 2003 Singapore. [16] Labeled graphs and Dynamic link matching for Face Recognition and Scene Analysis by Laurenz Wiskott.(PhD thesis) [17] L. Torres, Is there any hope for face recognition?, Proc. of the 5 th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2004, 2123 April 2004, Lisboa, Portugal [18] FRVT 2006 and ICE 2006 LargeScale Results [19] The FERET Evaluation Methodology for Facerecognition Algorithms, by P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, IEEE trans. Pattern Analysis and Machine Intelligence. (625KB) [20] Jorge Orts, “ECE533 – Image Processing Project”, Face Recognition Techniques, The University of Wisconsin. [21] Wlodzimierz M. Baranski, Andrzej WytyczakPartyka, Tomasz Walkowiak, “Computational complexity reduction in PCAbased face recognition”, Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Poland. [22] Lindsay I Smith, “A tutorial on Principal Components Analysis”, February 26, 2002. [23] Gregory Shakhnarovich and Baback Moghaddam, “Face Recognition in Subspaces”, Mitsubishi Electric Research Laboratories, May 2004. Published in: Handbook of Face Recognition, Eds. Stan Z. Li & Anil K. Jain, SpringerVerlag, 2004 [24]http://openbio.sourceforge.net/resources/eigenfaces/eigenfaceshtml/facesOp tions.html 68
[25] Adaptive median filters: new algorithms and results Hwang, H.; Haddad, R.A.; Image Processing, IEEE Transactions on, Volume: 4 Issue: 4, April 1995: Page(s): 499 502 [26] Selective removal of impulse noise based on homogeneity level information Gouchol Pok; JyhCharn Liu; Nair, A.S.; Image Processing, IEEE Transactions on , Volume: 12 Issue: 1, Jan. 2003: Page(s): 85 92 [27] S.Q. Yuan and Y.H. Tan, “Erratum to” impulse noise removal by a global local noise detector and adaptive median filter”: [signal processing 86 (8) (2006) 21232128],” Signal Processing, vol. 87, no. 5, p.1171, 2007. 69
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LITERATURE SURVEY OF AUTOMATIC FACE
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ABSTRACT There are three fundamenta
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2.3.2 Processing Steps before Face
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LIST OF TABLES Table Page 5.2.1 The
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12.1. Functioning principle of the
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the brain recognizes a face has lon
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CHAPTER II HOW A COMPUTER CAN PERFO
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Fig.2.2. Process of face recognitio
Page 17 and 18: non convex variations of face manif
Page 19 and 20: 3.1 Some Major difficulty for AFR C
Page 21 and 22: Gender researchers found surprising
Page 23 and 24: The idea of this method comes from
Page 25 and 26: Fig.4.3.1. Principal Components (PC
Page 27 and 28: and orientations (phase, amplitude)
Page 29 and 30: 4.14 Bayesian Framework A probabili
Page 31 and 32: feature line of that class. The que
Page 33 and 34: oughly the same scale), scale norma
Page 35 and 36: Table 5.2.4 Result for combined dat
Page 37 and 38: performance of the AutoAssociatio
Page 39 and 40: Furthermore, three of the seven alg
Page 41 and 42: Table 6.1 Various application of au
Page 43 and 44: CHAPTER VIII FOR PROSPECTIVE RESEAR
Page 45 and 46: Table 7.1 Internet Resources for Re
Page 47 and 48: etween the computer vision, signal
Page 49 and 50: So, in order to reconstruct the ori
Page 51: CHAPTER XII EIGENVECTORS AND EIGENV
Page 54 and 55: Step 4: Calculate the eigenvectors
Page 56 and 57: CHAPTER XV CLASSIFYING THE FACES Th
Page 58 and 59: CHAPTER XVII INTRODUCTION Images ar
Page 60: CHAPTER XIX THE ALGORITHM As in all
Page 63 and 64: If the condition Zmin < Zmed < Zmax
Page 65 and 66: (a) (b) Fig 21.3: (c) The image wit
Page 67: [5] M. Yang, D. J. Kriegman, and N.
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