LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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Every times the algorithm outputs a value, the window Sxy is moved to the next location in the image. The algorithm then is reinitialized and applied to the pixels in the new location. CHAPTER XXI EXPERIMENTAL RESULTS AND ANALYSIS The adaptive median filter is designed to remove impulsive noise from images. Therefore, our algorithm’s performance was first tested with basic salt and pepper noise with a noise density of 0.35. (a) (b) Fig21.1: (a) The image with ‘salt and pepper’ noise of 0.35 noise density. (b) Output of the standard median filter. Fig 21.2: Output of the adaptive median filter shows the improvement in performance with respect to the standard median filter. 64
(a) (b) Fig 21.3: (c) The image with ‘speckle’ noise of 0.6 variance. (d) Output of Adaptive Median Filter (a) (b) Fig 21.4: (c) Image with ‘Gaussian’ noise with 0 mean and 0.02 variance (d) Output of Adaptive Median Filter 65
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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
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: If the condition Zmin < Zmed < Zmax
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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