Multimodal Biometric and Machine Learning Technologies : Applications for Computer Vision

• Autore: Kumar Sandeep
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (324 pages)
• Soggetto topico: Biometric identification; Machine learning
• Altri autori: GhaiDeepika JainArpit TripathiSuman Lata RaniShilpa
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Multimodal Biometric in Computer Vision -- 1.1 Introduction -- 1.2 Importance of Artificial Intelligence, Machine Learning and Deep Learning in Biometric System -- 1.3 Machine Learning -- 1.3.1 Supervised vs Unsupervised Model -- 1.3.2 Classification and Regression Problem -- 1.4 Deep Learning -- 1.4.1 Steps to Create the Machine and Deep Learning Model -- 1.5 Related Work -- 1.5.1 Discussions -- 1.6 Biometric System -- 1.6.1 Biometrics in Physical Form -- 1.6.2 Biometrics with Behavior -- 1.6.3 Evaluation Parameters (Metrics) Used by Biometric Systems -- 1.7 Need for Multimodal Biometric -- 1.7.1 Perks of Multimodal Biometric -- 1.7.2 The General Outline of a Multimodal Biometric System -- 1.8 Databases Used by Biometric System -- 1.8.1 Confusion Matrix -- 1.9 Impact of DL in the Current Scenario -- 1.9.1 Computer Vision -- 1.9.2 Natural Language Processing -- 1.9.3 Recommendation System -- 1.9.4 Cyber Security -- 1.10 Conclusion -- References -- Chapter 2 A Vaccine Slot Tracker Model Using Fuzzy Logic for Providing Quality of Service -- 2.1 Introduction -- 2.2 Related Research -- 2.3 Novelty of the Proposed Work -- 2.3.1 Age -- 2.3.2 Availability of Vaccination Slots -- 2.3.3 Vaccination Status -- 2.4 Proposed Model -- 2.4.1 Role of the CoWIN App -- 2.4.2 Process for Signing Up for the CoWIN App -- 2.5 Proposed Fuzzy-Based Vaccine Slot Tracker Model -- 2.5.1 Fuzzy Rules -- 2.6 Simulation -- 2.7 Conclusion -- 2.8 Future Work -- References -- Chapter 3 Enhanced Text Mining Approach for Better Ranking System of Customer Reviews -- 3.1 Introduction -- 3.2 Techniques of Text Mining -- 3.2.1 Sentiment Analysis -- 3.2.2 Natural Language Processing -- 3.2.3 Information Extraction -- 3.2.4 Information Retrieval -- 3.2.5 Clustering -- 3.2.6 Categorization -- 3.2.7 Visualization. ; 3.2.8 Text Summarization -- 3.3 Related Research -- 3.4 Research Methodology -- 3.5 Conclusion -- References -- Chapter 4 Spatial Analysis of Carbon Sequestration Mapping Using Remote Sensing and Satellite Image Processing -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Materials -- 4.2.2 Methodology -- 4.2.2.1 Formula for the Mathematical Extraction of the Vegetation Area -- 4.3 Results -- 4.4 Conclusion -- Acknowledgment -- References -- Chapter 5 Applications of Multimodal Biometric Technology -- 5.1 Introduction -- 5.1.1 Benchmark for Effective Multimodal Biometric System -- 5.2 Components of MBS -- 5.2.1 Data Store(s) -- 5.2.2 Input Interface -- 5.2.3 Processing Unit -- 5.2.4 Output Interface -- 5.3 Biometrics Modalities -- 5.4 Applications of Multimodal Biometric Systems -- 5.4.1 MBS in Forensic Science -- 5.4.2 MBS in Government Applications -- 5.4.3 MBS in Enterprise Solutions and Network Infrastructure -- 5.4.4 MBS in Commercial Applications -- 5.5 Conclusion -- References -- Chapter 6 A Study of Multimodal Colearning, Application in Biometrics and Authentication -- 6.1 Introduction -- 6.1.1 Need for Multimodal Colearning -- 6.1.2 Why Multimodal Biometric Systems? -- 6.1.3 Multimodal Deep Learning -- 6.1.4 Motivation -- 6.2 Multimodal Deep Learning Methods and Applications -- 6.2.1 Multimodal Image Description (MMID) -- 6.2.2 Multimodal Video Description (MMVD) -- 6.2.3 Multimodal Visual Question Answering (MMVQA) -- 6.2.4 Multimodal Speech Synthesis (MMSS) -- 6.2.5 Multimodal Event Detection (MMED) -- 6.2.6 Multimodal Emotion Recognition -- 6.3 MMDL Application in Biometric Monitoring -- 6.3.1 Biometric Authentication System and Issues -- 6.3.2 Multimodal Biometric Authentication System and Benefits -- 6.4 Fusion Levels in Multimodal Biometrics -- 6.4.1 Fusion at Feature Level -- 6.4.2 Fusion at Matching Score Level. ; 6.4.3 Decision-Level Fusion -- 6.5 Authentication in Mobile Devices Using Multimodal Biometrics -- 6.5.1 Categories of Multimodal Biometrics -- 6.5.2 Benefits of Multimodal Biometrics in Mobile Devices -- 6.6 Challenges and Open Research Problems -- 6.7 Conclusion -- References -- Chapter 7 A Structured Review on Virtual Reality Technology Application in the Field of Sports -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Conclusion -- References -- Chapter 8 A Systematic and Structured Review of Fuzzy Logic-Based Evaluation in Sports -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Conclusion -- References -- Chapter 9 Machine Learning and Deep Learning for Multimodal Biometrics -- 9.1 Introduction -- 9.2 Machine Learning Using Multimodal Biometrics -- 9.2.1 Main Machine Learning Algorithms -- 9.2.2 A Hybrid Model -- 9.2.3 Semisupervised Learning Method -- 9.2.4 EEG-Based Machine Learning -- 9.3 Deep Learning Using Multimodal Biometrics -- 9.3.1 Based on Score Fusion -- 9.3.2 Deep Learning for Surveillance Videos -- 9.3.3 Finger Vein and Knuckle Print-Based Deep Learning Approach -- 9.3.4 Facial Video-Based Deep Learning Technique -- 9.3.5 Finger Vein and Electrocardiogram-Based Deep Learning Approach -- 9.4 Conclusion -- References -- Chapter 10 Machine Learning and Deep Learning: Classification and Regression Problems, Recurrent Neural Networks, Convolutional Neural Networks -- 10.1 Introduction -- 10.2 Classification of Machine Learning -- 10.3 Supervised Learning -- 10.3.1 Regression -- 10.3.2 Fuzzy Classification -- 10.3.3 Bayesian Networks -- 10.3.4 Decision Trees -- 10.3.5 Artificial Neural Network -- 10.3.6 Classification -- 10.4 Unsupervised Learning -- 10.5 Reinforcement Learning -- 10.6 Hybrid Approach -- 10.6.1 Semisupervised Learning -- 10.6.2 Self-Supervised Learning -- 10.6.3 Self-Taught Learning -- 10.7 Other Common Approaches. ; 10.7.1 Multitask Learning -- 10.7.2 Active Learning -- 10.7.3 Outline Learning -- 10.7.4 Transfer Learning -- 10.7.5 Federated Learning -- 10.7.6 Ensemble Learning -- 10.7.7 Adversarial Learning -- 10.7.8 Meta-Learning -- 10.7.9 Targeted Learning -- 10.7.10 Concept Learning -- 10.7.11 Bayesian Learning -- 10.7.12 Inductive Learning -- 10.7.13 Multimodal Learning -- 10.7.14 Curriculum Learning -- 10.8 DL Techniques -- 10.8.1 Recurrent Neural Network (RNN) -- 10.8.2 Convolutional Neural Network -- 10.8.3 Real-Time Applications of DL -- 10.9 Conclusion -- Acknowledgment -- References -- Chapter 11 Handwriting and Speech-Based Secured Multimodal Biometrics Identification Technique -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Method -- 11.3.1 SVM-Based Implementation -- 11.3.2 DTW-Based Implementation -- 11.3.3 CNN-Based Method -- 11.3.4 Proposed Model Implementation -- 11.4 Results and Discussion -- 11.4.1 Data Exploitation -- 11.4.2 Data Sets Used -- 11.4.3 Validation and Training -- 11.4.4 Results on CNN-Based Methods -- 11.4.5 Results of Deep Learning-Based Method -- 11.4.6 Results of the Proposed Method -- 11.4.7 Measure of Accuracy -- 11.5 Conclusion -- References -- Chapter 12 Convolutional Neural Network Approach for Multimodal Biometric Recognition System for Banking Sector on Fusion of Face and Finger -- 12.1 Introduction -- 12.2 Literature Work -- 12.3 Proposed Work -- 12.3.1 Pre-Processing -- 12.3.2 Feature Extraction -- 12.3.3 Classification -- 12.3.4 Ensemble -- 12.4 Results and Discussion -- 12.4.1 Data Set Used -- 12.4.2 Evaluation Parameter Used -- 12.4.3 Comparison Result -- 12.5 Conclusion -- References -- Chapter 13 Secured Automated Certificate Creation Based on Multimodal Biometric Verification -- 13.1 Introduction -- 13.1.1 Background -- 13.2 Literature Work -- 13.3 Proposed Work -- 13.4 Experiment Result. ; 13.5 Conclusion and Future Scope -- References -- Chapter 14 Face and Iris-Based Secured Authorization Model Using CNN -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Proposed Methodology -- 14.3.1 Pre-Processing -- 14.3.2 Convolutional Neural Network (CNN) -- 14.3.3 Image Fusion -- 14.4 Results and Discussion -- 14.5 Conclusion and Future Scope -- References -- Index -- EULA.
• Sommario/riassunto: This book explores the integration of multimodal biometric technologies with machine learning and deep learning techniques, focusing on applications in computer vision. It covers various aspects of biometric systems, including physical and behavioral biometrics, and discusses their importance in contemporary technological advancements. The text delves into the perks and challenges of multimodal biometric systems, highlighting their applications in fields such as forensic science, government, and commercial enterprises. Additionally, it examines advanced machine learning methods and their role in enhancing biometric systems, along with the potential future developments in these technologies. The book is intended for professionals and researchers in the fields of computer vision, biometrics, and machine learning.
• Titolo autorizzato: Multimodal Biometric and Machine Learning Technologies
• ISBN: 9781119785491; 1119785499; 9781119785484; 1119785480
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911019566703321
• Lo trovi qui: Univ. Federico II