LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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5.4 On the Combined Data Base…………………………………………...28 CHAPTER VI. EVALUATION OF FACE RECOGNITION SYSTEMS……...….29 CHAPTER VII. APPLICATIONS……………………………………………..…...…31 CHAPTER VIII. FOR PROSPECTIVE RESEARCHERS…………………………33 CHAPTER IX. EIGENFACE APPROACH OF FACE RECOGNITION…………37 CHAPTER X. HOW DOES IT WORKS……………………………………………38 CHAPTER XI. OVERVIEW OF THE ALGORITHM…………………………….…40 CHAPTER XII. EIGENVECTORS AND EIGENVALUES…………………………41 CHAPTER XIII. MATHEMETICAL CALCULATIONS………………………….….43 13.1 Eigenface Calculation………………………………………………..…43 CHAPTER XIV. IMPROVEMENT OF THE ALGORITHM…………...……………45 CHAPTER XV. CLASSIFYING THE FACES………………………………………46 CHAPTER XVI. CONCLUSION……………………...………………………….......47 CHAPTER XVII. INTRODUCTION…………………………………………………..48 CHAPTER XVIII. ADAPTIVE MEDIAN FILTER……………………………………49 CHAPTER XIX. THE ALGORITHM……………………….…………………..……50 CHAPTER XX. HOW IT WORKS………………………………………………..…51 CHAPTER XXI. EXPERIMENTAL RESULTS AND ANALYSIS…………………53 CHAPTER XXII. CONCLUSION………………………………….…………………55 REFERENCES…………………………………………………………………..……55 6
LIST OF TABLES Table Page 5.2.1 The Original Data Bases and Their Variations………………………..……24 5.2.2 The “Trimmed” Data Bases and Their Variations………………………..…24 5.2.3 Classification Results for Four Individual Data Bases……………………..25 5.2.4 Result for combined databases………………………………………………26 6.1 Various application of automatic face recognition……………………..……32 7.1 Internet Resources for Research and Databases………………………..…35 7.2 Available Commercial Face Recognition Systems…………………………36 7
Page 1 and 2: LITERATURE SURVEY OF AUTOMATIC FACE
Page 3 and 4: ABSTRACT There are three fundamenta
Page 5: 2.3.2 Processing Steps before Face
Page 9 and 10: 12.1. Functioning principle of the
Page 11 and 12: the brain recognizes a face has lon
Page 13 and 14: 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 and 66:
(a) (b) Fig 21.3: (c) The image wit
Page 67 and 68:
[5] M. Yang, D. J. Kriegman, and N.
Page 69:
[25] Adaptive median filters: new a
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LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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