Computational intelligence for human action recognition

Cover......Page 1
Half Title......Page 2
Series Page......Page 3
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Contents......Page 8
Preface......Page 12
Editors......Page 16
Contributors......Page 20
Chapter 1: Recent Advancements in Automatic Sign Language Recognition (SLR)......Page 22
  1.1.1 Common Challenges Faced in SLR......Page 23
  1.1.2 Approach......Page 24
  1.2.1 Hidden Markov Model......Page 25
  1.2.2 Gaussian Mixture Model......Page 28
  1.2.3 Neural Networks......Page 30
  1.2.4 Convolutional Neural Network: Hidden Markov Model......Page 31
  1.2.5 3D Convolutional Neural Network......Page 33
  1.2.6 Restricted Boltzmann Machine......Page 35
  1.2.7 Adaptive Pooling and Capsule Network......Page 37
  1.3.1 Hand Gesture Recognition System......Page 38
 1.4 CONCLUSION AND FUTURE SCOPE......Page 39
 BIBLIOGRAPHY......Page 41
Chapter 2: Distance-ShapeTexture Signature Trio for Facial Expression Recognition......Page 46
 2.1 INTRODUCTION......Page 47
 2.2 OVERVIEW OF THE PROPOSED SYSTEM......Page 48
 2.3 FACIAL LANDMARK DETECTION......Page 49
   2.3.1.1 Distance Signature......Page 51
   2.3.2.1 Shape Signature......Page 52
  2.3.4 Local Binary Pattern......Page 53
   2.3.4.1 Texture Signature......Page 54
  2.4.1 Stability Index of Distance-Shape-Texture signature trio......Page 55
 2.5 FEATURE SELECTION OF DISTANCE-SHAPE-TEXTURE SIGNATURE TRIO......Page 56
  2.6.1 Multilayer Perceptron......Page 57
  2.6.3 Radial Basis Network......Page 58
  2.7.1 Experiment on CK+ Database......Page 59
  2.7.2 Experiment on JAFFE Dataset......Page 61
  2.7.3 Experiment on MMI Database......Page 62
  2.7.4 Experiment on MUG Database......Page 64
  2.7.5 Comparison Analysis with Three Artificial Networks and State-of-the-Arts......Page 67
 BIBLIOGRAPHY......Page 68
Chapter 3: Face Expression Recognition using Side Length Features Induced by Landmark Triangulation......Page 74
 3.1 INTRODUCTION......Page 75
 3.2 RELATED WORKS......Page 76
 3.4 MOTIVATION......Page 77
  3.5.1 Facial Component Detection......Page 78
  3.5.2 Formation of Triangles......Page 79
  3.5.3 Feature Extraction......Page 80
  3.5.4 Classification Learning......Page 83
  3.6.1 Results on CK+ Database......Page 84
  3.6.2 Results on JAFFE database......Page 85
  3.6.3 Results on MMI database......Page 87
  3.6.4 Results on MUG database......Page 88
 3.8 ACKNOWLEDGMENTS......Page 90
 BIBLIOGRAPHY......Page 91
Chapter 4: A Study on the Influence of Angular Signature of Landmark Induced Triangulation in Recognizing Changes in Human Emotion......Page 94
 4.1 INTRODUCTION......Page 95
 4.2 PROPOSED METHOD......Page 96
  4.2.1 Landmark Identification......Page 97
  4.2.2 Geometric Feature Extraction......Page 100
   4.2.2.1 Formation of Angular Signature Matrix (ASM) by Triangulation mechanism......Page 102
 4.3 RESULTS AND DISCUSSION......Page 105
  4.3.1 Experiment on CK+ Database......Page 106
  4.3.2 Experiment on MUG Database......Page 107
  4.3.3 Experiment on MMI Database......Page 110
 4.4 COMPARISON WITH OTHER WORK......Page 114
 4.5 CONCLUSION......Page 116
 4.6 ACKNOWLEDGMENT......Page 119
 BIBLIOGRAPHY......Page 120
Chapter 5: A Behavioural Model for Persons with Autism Based on Relevant Case Study......Page 124
 5.1 INTRODUCTION......Page 125
 5.2 REVIEW OF RELATED WORKS......Page 128
  5.3.1 Study Design......Page 130
  5.3.2 Participants......Page 131
  5.3.3 Study Assessment Scale......Page 132
  5.3.5 Clinical Modules......Page 133
 5.4 RESULTS AND DISCUSSION......Page 134
 FURTHER READING......Page 139
Index......Page 146