Statistical and Neural Classifiers

For more info about our services contact : help@bestpfe.com

Table of contents

INTRODUCTION
0.1 Problem Statement
0.1.1 Biometric Systems
0.1.2 Statistical and Neural Classifiers
0.1.3 Adaptive Ensembles
0.2 Objective and contributions
0.3 Organization of the Thesis
CHAPTER 1 AN ADAPTIVE CLASSIFICATION SYSTEM FOR VIDEO-BASED FACE RECOGNITION
1.1 Introduction
1.2 Biometrics and face recognition from video sequences
1.3 Adaptive classification system
1.3.1 Long term memory
1.3.2 Fuzzy ARTMAP Neural Networks
1.3.3 Dynamic particle swarm optimization
1.4 Experimental Methodology
1.4.1 Video Data bases
1.4.2 Incremental learning scenarios
1.4.2.1 Enrollment
1.4.2.2 Update
1.4.3 Experimental protocol
1.5 Results and Discussion
1.5.1 Experiment (A) – Impact of the LTM for validation data
1.5.1.1 Enrollment scenario
1.5.1.2 Update scenario
1.5.2 Experiment (B) – Impact of dynamic optimization
1.5.2.1 Enrollment scenario
1.5.2.2 Update scenario
1.6 Conclusion
CHAPTER 2 EVOLUTION OF HETEROGENEOUS ENSEMBLES THROUGH DYNAMIC PARTICLE SWARM OPTIMIZATION FOR VIDEO-BASED FACE RECOGNITION
2.1 Introduction
2.2 An adaptive multiclassifier system
2.2.1 Fuzzy ARTMAP neural network classifiers
2.2.2 Dynamic particle swarm optimization
2.3 Strategy for evolving heterogeneous ensemble of FAM networks
2.3.1 Generation and evolution of heterogeneous classifier pools
2.3.2 Selection of diversified ensembles
2.4 Experimental methodology
2.4.1 Application–face recognition in video
2.4.2 Video data bases
2.4.3 Incremental learning scenarios
2.4.3.1 Enrollment
2.4.3.2 Update
2.4.4 Experimental protocol
2.4.5 Performance evaluation and diversity indicator
2.5 Results and discussion
2.5.1 Performance for single images (ROIs)
2.5.2 Performance for video-streams (multiple ROIs)
2.5.3 Particle diversity -vs- classifier diversity
2.6 Conclusion
CHAPTER 3 DYNAMIC MULTI-OBJECTIVE EVOLUTION OF CLASSIFIER ENSEMBLES APPLIED TO VIDEO-BASED FACE RECOGNITION
3.1 Introduction
3.2 Adaptive biometrics and video face recognition
3.3 Adaptive classifier ensembles
3.3.1 An adaptive multiclassifier system
3.3.2 Fuzzy ARTMAP neural network classifiers
3.3.3 Adaptation as a dynamic MOO problem
3.4 Evolution of incremental learning ensembles
3.4.1 ADNPSO incremental learning strategy
3.4.2 Aggregated dynamical niching PSO
3.4.3 Specialized archive and ensemble selection
3.5 Experimental methodology
3.5.1 Video data bases
3.5.2 Incremental learning scenarios
3.5.2.1 Enrollment
3.5.2.2 Update
3.5.3 Experimental protocol
3.5.4 Performance evaluation
3.6 Results and discussion
3.6.1 Performance during video-based face recognition
3.6.2 Swarm and archive evolution during optimization
3.7 Conclusion
CONCLUSION
APPENDIX I ANALYSIS OF THE LEARN++ ALGORITHM FOR VIDEO-BASED
FACE RECOGNITION
APPENDIX II INCREMENTAL LEARNING AS A DYNAMIC OPTIMIZATION PROBLEM 
BIBLIOGRAPHY