||Face Recognition under Illumination Variation by Using Retinex Algorithm
||Department of Electrical Engineering
local binary patterns
linear discrimination analysis
Face recognition has been an active research area due to its wide range of application in information security, video surveillance systems, human-computer interaction, and criminal verification.
Illumination variation, facial expression, and pose variation remain a persistent challenge in face recognition. In the thesis, we proposed a face recognition system which can resolve the problem caused by illumination variation. In our method, Retinex algorithm is adopted to remove the shadow of face firstly, and Local binary pattern is used to describe face feature. We propose global and local discriminative features for face recognition under various facial conditions based on robust feature description and feature extraction.
Experimental results demonstrate that our method can improve face recognition rate when amount of training images limited.
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 literature Survey 2
1.3 Organization of The Thesis 6
Chapter 2 Face Recognition System Overview 7
2.1 Image Normalization 7
2.1.1 Retinex Algorithm 7
2.1.2 Single-Scale Retinex 8
2.1.3 Multi-Scale Retinex 9
2.2 Feature Description 11
2.2.1 Histogram Equalization 11
2.2.2 Local Binary Patterns 13
2.2.3 Local Ternary Patterns 14
2.3 Feature Extraction 16
2.3.1 Principal Component Analysis 16
2.3.2 Linear Discriminant Analysis 20
2.3.3 Locality Preserving Projections 24
2.4 Classifier 27
2.4.1 k-Nearest Neighbor 27
2.4.2 Support Vector Machine 28
Chapter 3 Proposed Methods 33
3.1 Proposed Methods Flowchart 33
3.2 Image Normalization 34
3.3 Global and Local Discriminative Feature 35
3.4 Classifier 37
3.5 Global and Local Discriminative Feature Framework 38
3.6 Training Process of Face Recognition 39
3.7 Testing Process of Face Recognition 41
Chapter 4 Experimental Results 43
4.1 Face Image Database 43
4.2 Experience Results 43
4.2.1 Results by using Retinex Algorithm 44
4.2.2 Results of Combination Classifier 47
Chapter 5 Conclusions 55
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