LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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CHAPTER XXII CONCLUSION Still Few faces are there which really tough to distinguished for instance twin siblings. Building a system that can distinguish between the two lookalikes can be a challenge. Moreover we want a Computer that can recognize someone, even though there is only one picture of that person on file, and it was taken at a different angle, in different lighting, or they were wearing sunglasses as like human being. May be in near future you can imagine your desktop PC saying, ‘you seem upset, what can I do to help? REFERENCE [1] Handbook of Face Recognition by Stan Z. Li Anil K. Jain. [2] Turk, M. A., Pentland, A. P., Eigenfaces for recognition, 1991, Cognitive Neurosci., V. 3, no.1, pp.7186. [3] M. Turk, "A Random Walk Through Eigenface", IEICE Trans. INF. & SYST., Vol. E84D, No. 12, Dec. 2001. [4] Zhao, W., Chellappa, R., Phillips, P. J., Rosenfeld, A., 2003, Face recognition: A literature survey, ACM Computing Surverys (CSUR), V. 35, Issue 4, pp. 399458. 66
[5] M. Yang, D. J. Kriegman, and N. Ahuja, "Detecting Face in Images: A Survey", IEEE Trans. on PAMI, Vol. 24, No. 1, pp. 3458, Jan. 2002. [6] Survey paper: Face Detection and Face Recognition By Hyun Hoi James Kim [7] Face Recognition: a Summary of 1995 – 1997 by Thomas Fromherz [8] Nefian, A.V. et al., 1998, Hidden markov models for face recognition, In Proceedings, International Conference on Acoustics, Speech and Signal Proceeding, 2721 2724 [9] Face recognition: eigenface, elastic matching and neural nets Jun Zhang; Yong Yan; Lades, M. Proceedings of the IEEE Volume 85, Issue 9, Sep 1997 Page(s):1423 – 1435 [10] Thomas Fromherz, Peter Stucki, Martin Bichsel "A Survey of Face Recognition", MML Technical Report, No 97.01, Dept. of Computer Science, University of .Zurich. [11] Y.L. Tian and R. Bolle. Automatic detecting neutral face for face authentication. In Proceedings of AAAI03 Spring Symposium on Intelligent Multimedia Knowledge Management, CA, 2003. [12] Y.L. Tian, T. Kanade, and J. Cohn. Recognizing action units for facial expression analysis. IEEE Trans. on Pattern Analysis and Machine Intell., 23(2):1–19, 2001. [13] Comprehensive Database for Facial Expression Analysis BY Takeo Kanade Jeffrey F. Cohn, Yingli Tian [14] 5. V. Blanz and T. Vetter. A morphable model for the synthesis of 3d faces. In Computer Graphics Proceedings, Annual Conference Series (SIGGRAPH), 1999. 67
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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
Page 15 and 16: 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: (a) (b) Fig 21.3: (c) The image wit
Page 69: [25] Adaptive median filters: new a
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