Multimodal Biometric Systems: Security and Applications

✍ Scribed by Rashmi Gupta; Manju Khari

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 167
• Category: Library

✦ Synopsis

Many governments around the world are calling for the use of biometric systems to provide crucial societal functions, consequently making it an urgent area for action. The current performance of some biometric systems in terms of their error rates, robustness, and system security may prove to be inadequate for large-scale applications to process millions of users at a high rate of throughput. This book focuses on fusion in biometric systems. It discusses the present level, the limitations, and proposed methods to improve performance. It describes the fundamental concepts, current research, and security-related issues. The book will present a computational perspective, identify challenges, and cover new problem-solving strategies, offering solved problems and case studies to help with reader comprehension and deep understanding. This book is written for researchers, practitioners, both undergraduate and post-graduate students, and those working in various engineering fields such as Systems Engineering, Computer Science, Information Technology, Electronics, and Communications.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors’ Biographies
Contributors
Chapter 1 Deep Learning-Based Computer Vision: Security, Application and Opportunities
1.1 Introduction
1.1.1 Tasks in CV
1.1.2 Applications of CV
1.1.3 Object Detection
1.1.3.1 Model Output
1.1.4 Image Classification
1.1.5 Image Segmentation
1.1.6 Deep Learning
1.1.7 Samples of Deep Learning at Work
1.1.8 Convolution Neural Networks
1.2 Research Challenges of CV
1.3 Object Detection Methods
1.3.1 YOLO (You Only Look Once)
1.3.2 Mask R-CNN
1.3.3 SDD (Single Shot Multi-Box Detector)
1.3.4 Retina Net
1.3.5 Faster R-CNN
1.3.6 Cascade R-CNN
1.4 Image Classification Models
1.4.1 AlexNet
1.4.2 VGGNet
1.4.3 ResNet
1.4.4 SqueezNet
1.4.5 GoogleNet
1.5 Research Gaps
1.6 Conclusion and Future Work
References
Chapter 2 Recognition of Foggy Image for Surveillance Application
2.1 Introduction
2.2 Literature Review
2.3 Proposed Method
2.4 Result Analysis
2.4.1 Qualitative Analysis
2.4.2 Quantitative Analysis
2.5 Conclusion
References
Chapter 3 FishNet: Automated Fish Species Recognition Network for Underwater Images
3.1 Introduction
3.2 Literature Survey
3.3 Proposed Approach
3.3.1 Architecture of AlexNet
3.3.2 Fine Tuning the AlexNet Neural Network
3.3.3 Architecture of ResNet50
3.3.4 ResNet50 as Feature Extractor (Fish_Net_SVM)
3.4 Results
3.4.1 Data Set
3.4.2 Result Evaluation and Comparisons
3.5 Conclusion
References
Chapter 4 Person Identification in UAV Shot Videos by Using Machine Learning
4.1 Introduction
4.2 Related Work
4.2.1 Data Set
4.3 Proposed Work
4.4 Empirical Evaluation
4.4.1 Data Pre-Processing and Balancing of Data
4.4.2 Splitting of the Data Set
4.4.3 Choosing the Model
4.4.4 Face Detection Techniques
4.4.5 Viola Jones
4.4.6 Local Binary Pattern
4.4.7 Commercial-Off-the-Shelf System
4.4.8 Face Detection
4.4.9 Face Recognition
4.5 Conclusion and Future Work
References
Chapter 5 ECG-Based Biometric Authentication Systems Using Artificial Intelligence Methods
5.1 Introduction
5.2 Biometric Identification
5.2.1 Biometric Identification System Architecture
5.3 ECG-Based Biometric Identification
5.3.1 ECG Physiology
5.3.2 ECG Waveform
5.4 ECG signal Processing for Biometric Systems
5.4.1 Denoising
5.4.2 Segmentation
5.4.3 Feature Extraction
5.4.4 Feature Selection
5.5 ECG-Based Biometric Authentication Systems
5.6 Artificial Intelligence Methods for ECG-Based Biometric Authentication Systems
5.7 Conclusion
References
Chapter 6 False Media Detection by Using Deep Learning
6.1 Introduction
6.2 Related Work
6.3 Proposed Detection Algorithm for Detection of Fake Media
6.3.1 Background of Proposed Work
6.3.1.1 Face-Re Enactment
6.3.1.2 Deep-Fake
6.3.1.3 Generative Adversarial Network
6.3.2 Experimental Evaluation of Proposed Work
6.3.2.1 Output
6.4 Conclusion and Future Work
References
Chapter 7 Evaluation of Text-Summarization Technique
7.1 Introduction
7.2 Related Work
7.2.1 Term Frequency (Word Frequency)
7.2.2 Term Frequency-Inverse Document Frequency
7.2.3 Text Rank
7.2.4 Summa
7.2.5 Sentence Embeddings
7.3 Empirical Evidence
7.3.1 Corpus
7.3.2 Pre-Processing
7.4 Evaluation Method
7.5 Evaluation
7.5.1 Evaluation
7.5.1.1 TF-IDF
7.5.2 Metrics Implementation
7.5.3 Results
7.5.4 Discussion
7.6 Conclusion and Future Research
References
Chapter 8 Smart Metro Ticket Management by Using Biometric
8.1 Introduction
8.1.1 Metro Ticketing System
8.2 Related Work
8.2.1 Object Detection
8.2.2 Sign Language Recognition
8.2.3 Smart Parking System
8.3 Proposed Model for Metro Ticketing System
8.3.1 Architecture
8.3.1.1 React JS Application
8.3.1.2 Image Matching Model
8.3.1.3 API Gateway
8.3.1.4 Flask Server
8.4 Results and Analysis
8.4.1 Technology Stack
8.4.2 Use Case
8.5 Conclusion and Future Scope
References
Chapter 9 Internet of Things: Security Issues, Challenges and Its Applications
9.1 Introduction
9.2 Architecture of IoT
9.2.1 Perception Layer
9.2.2 Network Layer
9.2.3 Application Layer
9.3 Security Issues and Features of IoT
9.3.1 Security Features of IoT
9.3.2 IoT Security Risks
9.3.2.1 Lack of Observance on the Part of IoT Manufacturers
9.3.2.2 Lack of User Knowledge and Awareness
9.3.2.3 Issue of IoT Security in Device Update Management
9.3.2.4 Lack of Physical Hardening
9.3.2.5 Botnet Attacks
9.3.2.6 Industrial Espionage and Eavesdropping
9.3.2.7 Hijacking Your IoT Devices
9.3.2.8 Data Reliability Risks in Healthcare for IoT Security
9.3.2.9 Rogue IoT Devices
9.3.2.10 Crypto Mining with IoT Bots
9.4 IoT Challenges
9.5 Different Types of Attacks and Possible Solutions
9.6 IoT Applications
9.6.1 IoT in Industries
9.6.2 IoT in Personal Medical Devices
9.6.3 Smart Home Using IoT
9.6.4 IoT in Biometrics
9.7 Conclusion
References
Chapter 10 Wireless Sensor Network for IoT-Based ECG Monitoring System Using NRF and LabVIEW
10.1 Introduction
10.2 Proposed System
10.3 Hardware and Software Description
10.4 Circuit Description
10.5 Working of Proposed System
10.6 Results
10.7 Conclusion
10.8 Future Scope
References
Chapter 11 Towards Secure Deployment on the Internet of Robotic Things: Architecture, Applications, and Challenges
11.1 Introduction
11.1.1 Motivation and Contribution
11.2 Literature Survey
11.3 Internet of Robotic Things
11.4 Emerging IoRT Technologies
11.4.1 Sensors and Actuators
11.4.2 Communication Technologies
11.4.3 Connected Robotic Things
11.4.4 Virtual and Augmented Reality
11.4.5 Voice Recognition and Voice Control
11.5 Architecture of IoRT
11.5.1 Hardware Layer
11.5.2 Network Layer
11.5.3 Internet Layer
11.5.4 Infrastructure Layer
11.5.5 Application Layer
11.6 Applications of IoRT
11.6.1 Smart Home
11.6.2 Smart Office
11.6.3 Smart Workshop/Factory
11.6.4 Smart Nursing House
11.7 Research Challenges
11.7.1 Security
11.7.2 Authentication Issues
11.7.3 Computational Problem
11.7.4 Optimization
11.8 Conclusion
References
Index