Advanced Methods for Human Biometrics

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 (Masšek, 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)
		6.3.1 Grasshopper Optimization Algorithm (GOA)
		6.3.2 GOA-SVM
	6.4 Experimental Results
	6.5 Conclusion
	References
Part III Algorithm Based Methods of Multimodal Authentication
7 Tracklet and Signature Representation Using Part Appearance Mixture Approach in the Context of Multi-shot Person Re-Identification
	7.1 Introduction
	7.2 Main Challenges of Person Re-ID
	7.3 Related Works
	7.4 Person Re-ID Process
		7.4.1 Detection
		7.4.2 Multi-object Tracking
	7.5 Part Appearance Mixture (PAM) Approach
		7.5.1 Signature Representation
		7.5.2 Similarity Metric for Signature Representation
		7.5.3 Distance Computation Between Signatures
	7.6 Experiments and Results
		7.6.1 Datasets
		7.6.2 Performance Evaluation
		7.6.3 Evaluation of Signature Representation Quality
	7.7 Conclusion
	References
8 A Novel Approach for Speaker Recognition in Degraded Conditions
	8.1 Introduction
	8.2 Related Works
	8.3 Proposed Approach
		8.3.1 Pre-processing
		8.3.2 Feature Extraction
		8.3.3 Classification
	8.4 Experimental Results
	8.5 Conclusion
	References
9 Visual Methods for Sign Language Recognition: A Modality-Based Review
	9.1 Introduction
	9.2 Human Actions Recognition Pipeline
	9.3 Unimodal Methods
		9.3.1 Recognition from Joint Streams
		9.3.2 Recognition from RGB Streams
		9.3.3 Recognition from Depth Streams
		9.3.4 Unimodal Temporal Segmentation Approaches
	9.4 Multi-modal Methods
		9.4.1 Multi-modal Datasets for HAR
		9.4.2 Multi-modal Fusion Approaches
		9.4.3 Multi-modal Datasets for 3D FEs Recognition
		9.4.4 Multi-modal Approaches for 3D FEs Recognition
	9.5 Main Contributions Related to SL Recognition
		9.5.1 SL Datasets
		9.5.2 SL Visual-Recognition Based Works
	9.6 Conclusion and Discussion
		9.6.1 Datasets Level
		9.6.2 Approaches Level
		9.6.3 Commercial Solutions Level
	References
10 A Software Architecture for Developing Disease Registries
	10.1 Introduction
	10.2 Related Work
		10.2.1 Technology
		10.2.2 Data
		10.2.3 Knowledge
		10.2.4 Analytics
		10.2.5 Services
		10.2.6 Security
		10.2.7 Sharing
	10.3 Proposed Software Architecture
		10.3.1 Technology Layer
		10.3.2 Data Layer
		10.3.3 Knowledge Layer
		10.3.4 Analytics Layer
		10.3.5 Service Layer
		10.3.6 Security and Privacy
		10.3.7 Sharing
		10.3.8 Interactions
	10.4 Use Cases
	10.5 Conclusion
	References
Part IV Biomedical Characteritics
11 3D Textures Analysis Based on Features Extraction
	11.1 Introduction
	11.2 Methods of Texture Measures
		11.2.1 Decimal Descriptor Patterns (DDP)
		11.2.2 Local Binary Patterns
		11.2.3 Grey Level Co-occurrence Matrix Method
	11.3 Experiments and Results
		11.3.1 Databases
		11.3.2 Phases of Simulation
		11.3.3 3D MR Brain Images Analysis
		11.3.4 3D Face Analysis
		11.3.5 Discussion
	11.4 Conclusion
	References
12 Image Processing and Analysis for Decision Making Applied to Melanoma
	12.1 Introduction
	12.2 About Melanoma
	12.3 Diagnostic Aid System Based on Score Computation
		12.3.1 Images Acquisition
		12.3.2 Images Pretreatment
		12.3.3 Lesion Detection
		12.3.4 Interpretation of Medical Images
	12.4 Diagnostic Aid System Based on Machine Learning
		12.4.1 Images Acquisition
		12.4.2 Pretreatment of Dermatoscopic Images
		12.4.3 Segmentation of Lesion Based on Region Growing Method
		12.4.4 Skin Lesion Analysis
	12.5 Experimental Results and Discussion
		12.5.1 Approach Based on the MultiOtsu Principle
		12.5.2 Approach Based on the Region Growing Method
		12.5.3 Evaluation and Discussion
	12.6 Conclusion
	References
13 Biomedical Computer Aided Design Systems: Application to Alzheimer Disease
	13.1 Introduction
	13.2 Proposed Methodology
	13.3 Previous Works
		13.3.1 Partial Least Square (PLS)
		13.3.2 Kernel Partial Least Square (KPLS)
	13.4 Proposed Downsized KPLS Method (DPLS)
	13.5 Optimization with Multi-objective Optimization Algorithm
		13.5.1 Principle
		13.5.2 Selection of Kernel Parameter with Multi-Objective Optimization Algorithm
	13.6 Classification Using Neural Networks
	13.7 Experiments
		13.7.1 Experiments on ADNI Dataset
		13.7.2 Experiments on OASIS Dataset
	13.8 Conclusion and Future Work
	References