LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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Approach to handle the nonlinearity: • Kernel techniques (kernel PCA and kernel LDA). • Local features analysis (LFA). • Gabor waveletbased features. • Local binary pattern (LBP). 18
3.1 Some Major difficulty for AFR CHAPTER III COMLEXITY <strong>OF</strong> AFR In field settings, face images are subject to a wide range of variations. These include pose or view angle, illumination, occlusion, facial expression, time delay between image acquisition, and individual differences. The scalability of face recognition systems to such factors is not well understood. Most research has been limited to frontal views obtained under standardized illumination on the same day with absence of occlusion and with neutral facial expression or slight smile. For faces to be a useful biometric, facial features used for face recognition should remain invariant to factors unrelated to person identity that modify face image appearance. While theory and some data suggest that many of these factors are difficult to handle, it is not clear where exactly the difficulties lie and what their causes may be. The influence of facial expression on recognition is not well understood. Previous research has been limited primarily to neutral expressions and slight smiles. Because facial expression affects the apparent geometrical shape and position of the facial features, the influence on recognition may be greater for geometry based algorithms than for holistic algorithms. Occlusion the performance of the face recognition algorithms under occlusion is in general poor. The face may be occluded by other objects in the scene or by sunglasses or other things. Occlusion may be unintentional or intentional. Under some conditions subjects may be motivated to thwart recognition efforts by covering portions of their face. Since in many situations, the goal is to recognize non or even uncooperating subjects, Time delay Faces change over time. There are changes in hair style, makeup, muscle tension and appearance of the skin, presence or absence of 19
Page 1 and 2: LITERATURE SURVEY OF AUTOMATIC FACE
Page 3 and 4: ABSTRACT There are three fundamenta
Page 5 and 6: 2.3.2 Processing Steps before Face
Page 7 and 8: LIST OF TABLES Table Page 5.2.1 The
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: non convex variations of face manif
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.
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[25] Adaptive median filters: new a
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