FACE RECOGNITION IN CRIMINAL INVESTIGATIONS (UNCONTROLLED ENVIRONMENT)

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Author

Presented To

Department of Electrical/Electronic Engineering

ABSTRACT
The application of face recognition through public surveillance cameras in criminal
investigations is non-intrusive, inconspicuous and faster compared with fingerprint
or DNA-sampling biometric evaluation methods. It assists the law enforcement
agency during the preliminary investigation to narrow down the candidate list of
suspects or person-of-interest. In this project, the fusion between Gabor filters and
Maximum Response (MR) filters with Random Forests classifier is proposed for face
recognition in criminal investigation. The proposed method differs from the
algorithms such as deep neural networks in which the deep neural networks require
larger training datasets. However, it is difficult to obtain large volume of the face
images of the same individuals in reality. Deep neural networks are also more
computation exhausting. The Gabor and MR filters are the facial features extractor.
The Gabor filters are the hybrid of the Gabor magnitude filters and oriented Gabor
phase congruency (OGPC) filters. Gabor magnitude filters produce the magnitude
response while the OGPC filters produce the phase response of Gabor filters. The
MR filters produce the edge- and bar-anisotropic filter responses and isotropic filter
responses.
The variable selection using Monte Carlo Uninformative Variable
Elimination Partial Least Squares Regression (MC-UVE-PLSR) is used to pick the
most useful features in order to save computation costs and time which is crucial in
criminal investigations. Random Forests is used in the classification of the generated
feature vectors. The algorithm is applied in uncontrolled environment where the
unconstrained parameters of facial images such as uncontrolled illumination, pose
and expression variations commonly present in the tape recorded by surveillance
cameras during criminal investigation. The algorithm performance is evaluated
using two unconstrained facial image databases: Labelled Faces in the Wild and
Unconstrained Facial Images (UFI). The images of the databases include different
illumination, face expressions and pose variations. The implemented method
achieved 81.28% and 67.33% of recognition rates and 97.07% and 93.06% of
Receiver Operating Characteristics (ROC) curve on LFW and UFI databases.
TABLE OF CONTENTS
DECLARATION iii
APPROVAL FOR SUBMISSION iv
ACKNOWLEDGEMENTS vi
ABSTRACT vii
TABLE OF CONTENTS viii
LIST OF TABLES xii
LIST OF FIGURES xiii
LIST OF SYMBOLS / ABBREVIATIONS xvi
LIST OF APPENDICES xvii
CHAPTER
1 INTRODUCTION 1
1.1 General Introduction 1
1.2 Importance of the Study 1
1.3 Problem Statement 2
1.4 Aims and Objectives 3
1.5 Scope and Limitation of the Study 4
1.6 Contribution of the Study 4
1.7 Outline of the Report 4
2 LITERATURE REVIEW 6
2.1 Introduction 6
2.1.1 Face Recognition System 6
2.1.2 Face Detection Method 10
2.1.3 Feature Extraction 12
2.1.4 Feature Selection 19
2.2 Face Recognition 21
2.2.1 Subspace-based Method 21
2.2.2 Learning-based Method 25
2.3 Summary 29
3 METHODOLOGY AND WORK PLAN 30
3.1 Introduction 30
3.2 Databases 32
3.2.1 Labelled Faces in the Wild (LFW) 32
3.2.2 Unconstrained Facial Images (UFI) 34
3.3 Image Pre-Processing 35
3.4 Feature Extraction 36
3.4.1 Gabor Filters 36
3.4.2 Oriented Gabor Phase Congruency Filters 39
3.4.3 Maximum Response Filters 40
3.5 Feature Selection 42
3.5 Feature Selection 42
3.5.1 Monte Carlo Uninformative Variable Elimination
Partial Least Squares Regression (MC-UVE-PLSR) 42
3.6 Classification and Prediction Using Learning Framework 43
3.6.1 Random Forests 44
3.7 Fusion of Gabor Filters and Maximum Response Filters 47
3.8 Software 49
4 RESULTS AND DISCUSSIONS 50
4.1 Introduction 50
4.2 The Pre-Processing Phase 50
4.2.1 Face Detection 50
4.2.2 Conversion into Grayscaled Format 51
4.2.3 Tan-Triggs Illumination Normalization 51
4.3 Feature Extraction 53
4.3.1 Gabor Filters 53
4.3.2 Maximum Response (MR) Filters 57
4.4 Feature Selection 58
4.5 Classification using Random Forests 61
4.6 Fusion of the Random Forest Prediction Scores of Gabor
Magnitude, OGPC and MR filters 64
4.7 Finalization of Parameters 65
4.8 Comparison with Other Existing Algorithms 68
4.8.1 The Use of Cross Validation in the Evaluation of
Algorithms 68
4.8.2 Labelled Faces in the Wild 70
4.8.3 Unconstrained Facial Images (UFI) 78
4.8.4 Summary of the Comparison of Algorithms using LFW
and UFI Databases 85
5 CONCLUSIONS AND RECOMMENDATIONS 86
5.1 Conclusions 86
5.2 Challenges and Recommendations for future work 86
REFERENCES

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