Advanced methods for human biometrics / / edited by Nabil Derbel and Olfa Kanoun

• Pubblicazione: Cham, Switzerland : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (305 pages)
• Disciplina: 006.248
• Soggetto topico: Biometric identification
• Persona (resp. second.): KanounOlfa; DerbelNabil
• Nota di contenuto: 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 (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.
• Titolo autorizzato: Advanced methods for human biometrics
• ISBN: 3-030-81982-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910502619903321
• Lo trovi qui: Univ. Federico II
• Serie: Smart Sensors, Measurement and Instrumentation