TİRYAKİ, VOLKAN MÜJDATRASOOL, MEGIR MOHAMMED RASOOL2019-11-292019-11-2920192019-07-04Rasool, Megir Mohammed Rasool, Face recognition using LBP, nLBP and αLBP algorithms, Siirt Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, 2019.https://hdl.handle.net/20.500.12604/2102Face recognition is of great interest because it is one of the most important image processing applications. Although the success rates of the studies in the literature are high, the performance in out-ofcontrol situations is still not better than human. There are many challenges to design an accurate and robust face recognition system, especially in non-restricted environments. In this thesis classical local binary pattern (LBP), neighborliness local binary pattern (nLBP) and αLBP were used for a face recognition problem. nLBP is formed according to the relationship between the neighbors around each pixel. nLBP has a distance parameter which specifies the distance between consecutive neighbors to be compared. Different patterns are obtained for different d parameter values. αLBP operator calculates the value of each pixel according to an angle value. Angle values can be α = 0, 45, 90 and 135 degrees. The ORL face database was used to test the proposed approaches. nLBP, αLBP and classical LBP features were extracted from face images and classified using knearest neighbor (kNN) and artificial neural network (ANN). 98.25% recognition rate was obtained using kNN with nLBP. A recognition rate of 88.50% was obtained with αLBP using ANN. The recognition rate of 83.50% was obtained with classical LBP. The proposed nLBP and αLBP approaches were found to be more successful than the classical LBP method. In the literature, the success rates obtained in the studies performed on the same ORL face database were compared with the success rates of the proposed approaches in this thesis study. As a result, the proposed LBP-based approaches achieved significant success in face recognition.TABLE OF CONTENTS THESIS NOTIFICATION............................................................................................................ ii ACKNOWLEDGEMENTS................................................... ABBREVIATIONS AND SYMBOL LISTS .............................................................................. ix Symbol Description.............................................................................................................. x ÖZET ........................................................................................................................................... xi ABSTRACT................................................................................................................................ xii 1. INTRODUCTION ................................................................................................................ 1 1.1. Background............................................................................................................................ 1 1.1.1. Why Face Recognition?...................................................................................................... 1 1.2. History of Face Recognition .................................................................................................. 2 1.3. Face Recognition ................................................................................................................... 3 1.4. Local Binary Pattern (LBP) ................................................................................................... 4 1.5 Why is LBP used in face recognition?.................................................................................... 4 1.6 Applying LBP in Facial Recognition...................................................................................... 4 1.7 Advantages of LBP................................................................................................................. 4 1.8 Problem Statement.................................................................................................................. 4 2. LITERATURE REVIEW ......................................................................................................... 6 2.1 Historical Background ............................................................................................................ 6 2.2 Time Evolution and Modern Techniques................................................................................ 6 2.3 Face Detection ........................................................................................................................ 6 2.4 Face Recognition .................................................................................................................... 7 2.5 Application of LBP Texture Features in Face Recognition .................................................... 7 2.6 Multi-scale LBP Histograms for Facial Recognition.............................................................. 8 2.7 An Extensive Approach to Near Infrared Face Recognition Grounded On ELBP................. 8 2.8 Multi-scale Block LBP for Face Recognition......................................................................... 8 2.9 Local Gabor Binary Pattern Histogram Sequence (LGBPHS) for Face Representation and Recognition................................................................................................................................... 9 3. MATERIALS AND METHODS............................................................................................ 10 3.1. Materials .............................................................................................................................. 10 3.2. Methods................................................................................................................................ 11 3.2.1.Conventional Local Binary Pattern Operator..................................................................... 11 3.2.2. Local binary patterns based on relations between neighbors: neighborliness LBP .......... 13 3.2.3. Local Binary Patterns based on angles : αLBP................................................................. 15 3.3. Proposed Face Recognition System..................................................................................... 17 3.3.1. Classification Methods...................................................................................................... 17 3.3.1.1. Artificial Neural Networks (ANN) ................................................................................ 17 3.3.1.2. kNN Method .................................................................................................................. 18 3.4. kNN algorithm steps............................................................................................................ 19 3.4.1. Pre-processing of data....................................................................................................... 20 vi 3.4.2. K number and its effect on classification.......................................................................... 20 3.4.3. Cross validity test.............................................................................................................. 21 3.4.5. Performance Measures...................................................................................................... 22 3.6. Accuracy .............................................................................................................................. 22 3.7. Error rate .............................................................................................................................. 22 3.8. Precision............................................................................................................................... 23 3.8.1. Recall ................................................................................................................................ 23 3.8.2. F-measure.......................................................................................................................... 23 4. RESULTS .............................................................................................................................. 24 4.1. Results for nLBP (neighborliness Local Binary Pattern)..................................................... 24 4.2. Results for αLBP.................................................................................................................. 27 4.3. Results for Classic LBP ....................................................................................................... 30 4.5. Comparison of Models......................................................................................................... 32 4.6. Comparison of Different Machine Learning Methods......................................................... 32 4.7. Comparison with the Studies in Literature........................................................................... 33 SRC, sparse representation based classification ................................................................. 33 Extended SRC..................................................................................................................... 33 5. CONCLUSION....................................................................................................................... 34 REFERENCES ........................................................................................................................... 36 CURRICULUM VITAE............................................................................................................. 40eninfo:eu-repo/semantics/openAccessMachine learningBiometric identificationClassificationMaster Thesis561838