Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system.
Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images. Some methods are exhibiting good results in certain conditions and others are good with different kinds of images. Image discriminating techniques are widely used for pattern and image analysis. Common discriminating methods are discussed.
Contents
1 INTRODUCTION
1.1 OVERVIEW
1.2 MOTIVATION
1.3 AIMS AND OBJECTIVES
1.4 EXPECTED OUTCOME
2 BACKGROUND
2.1 FACE DETECTION
2.2 CHALLENGES
2.3 FACE DETECTION METHODS
2.3.1 Knowledge Based Methods
2.3.2 Template based methods
2.3.3 Feature based methods
2.3.4 Appearance based methods
2.4 CONCLUSION
3 CASE STUDY
3.1 IMAGE DATABASE
3.2 IMAGE DISCRIMINATION TECHNIQUES
3.2.1 Principal Component Analysis
3.2.2 Linear Discriminating Analysis
3.3 PROPOSED SYSTEM
4 KERNEL METHODS
4.1 OVERVIEW
4.2 KERNEL FUNCTIONS
4.3 SUPPORT VECTOR MACHINES
4.3.1 Polynomial
4.3.2 Radial Based
4.3.3 Sigmoid
4.4 KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA)
4.5 KERNEL FISHER DISCRIMINANT ANALYSIS
4.6 COMPARISON OF PCA AND LDA
5 CONCLUSION
5.1 DISCUSSION
5.2 FUTURE WORK
6 REFERENCES
7 FIGURES &TABLES
Source: Blekinge Institute of Technology
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