Intro
Preface
Contents
Part I Authentication Based on Measurements of Human Characteristics
1 Efficient Fingerprint Analysis Based on Sweat Pore Map
1.1 Introduction
1.2 Related Works
1.3 Proposed Approach
1.3.1 Step 1: Pores Detection
1.3.2 Step 2: Features Extraction
1.3.3 Step 3: Pores Alignment
1.3.4 Step 4: Pores Matching
1.4 Experiments and Performance Evaluation
1.4.1 Data Base
1.4.2 Training and Test Process
1.4.3 Feature Matching
1.4.4 Performance Evaluation
1.5 Conclusion
References

2 Fingerprint Recognition Based on Level Three Features
2.1 Introduction
2.2 Biometry Background
2.2.1 Biometric Systems
2.2.2 Biology of the Fingerprint
2.3 Pores Detection
2.3.1 Related Works
2.3.2 Proposed Method
2.4 Pores Matching
2.4.1 Related Works
2.4.2 Proposed Method
2.5 Experimental Results
2.5.1 Database
2.5.2 Pores Detection
2.5.3 Recognition
2.6 Conclusion
References
3 Fractal Analysis for Iris Multimodal Biometry
3.1 Introduction
3.2 Related Works
3.3 Feature Extraction Based on Fractal Analysis

3.4 Uni-Modal Recognition System
3.4.1 PBMLTiris Database Description
3.4.2 Pre-processing
3.4.3 Iris Segmentation (Daugman's Operator)
3.4.4 Normalization Based on the Pseudo-Polar Method (Masak, ch3AmenispsbibspsMaek2003RecognitionOH)
3.4.5 Matching
3.5 Multi-modal Recognition System
3.5.1 Limitations of Uni-Modal Recognition System (Singh et al., ch3Amenispsbibspssingh2019comprehensive)
3.5.2 Fusion Sources
3.5.3 Fusion Levels
3.6 Experimental Results
3.6.1 Segmentation Results
3.6.2 Uni-Modal System Evaluation
3.6.3 Feature Level Fusion Results

3.6.4 Sensor Level Fusion Results
3.6.5 Score Level Fusion Results
3.7 Discussion and Conclusion
References
Part II Authentication by Biological Signals
4 Security with ECG Biometrics
4.1 Biometrics Definition
4.2 Biometrics with ECG
4.3 ECG Biometrics Approaches
4.3.1 Fiducial Approaches
4.3.2 Non-fiducial Approaches
4.4 ECG Signal Filters
4.5 ECG Biometric Classifiers
4.6 Evaluation of ECG Biometrics
4.7 Conclusion
References
5 ECG Biometric System for Human Recognition Based on the Possibility Theory
5.1 Introduction
5.2 Possibility Theory

5.2.1 Possibility Distribution
5.2.2 Transformation from Probability Distribution to Possibility Distribution
5.3 Methodology
5.3.1 ECG Signal Pre-processing
5.3.2 Feature Extraction
5.3.3 Possibility Theory Based ECG Classification
5.3.4 Experimental Results and Discussion
5.4 Conclusion
References
6 Surface EMG Based Biometric Person Authentication by a Grasshopper Optimized SVM Algorithm
6.1 Introduction
6.2 Biometry Based on sEMG Signals
6.3 Hybrid Grasshopper Optimization Algorithm and Support Vector Machine (GOA-SVM)