With an increasing demand for biometric systems in various industries, this book on multimodal biometric systems, answers the call for increased resources to help researchers, developers, and practitioners.
Multimodal biometric and machine learning technologies have revolutionized the field of security and authentication. These technologies utilize multiple sources of information, such as facial recognition, voice recognition, and fingerprint scanning, to verify an individual???s identity. The need for enhanced security and authentication has become increasingly important, and with the rise of digital technologies, cyber-attacks and identity theft have increased exponentially. Traditional authentication methods, such as passwords and PINs, have become less secure as hackers devise new ways to bypass them. In this context, multimodal biometric and machine learning technologies offer a more secure and reliable approach to authentication.
This book provides relevant information on multimodal biometric and machine learning technologies and focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity. The book provides content on the theory of multimodal biometric design, evaluation, and user diversity, and explains the underlying causes of the social and organizational problems that are typically devoted to descriptions of rehabilitation methods for specific processes. Furthermore, the book describes new algorithms for modeling accessible to scientists of all varieties.
Audience
Researchers in computer science and biometrics, developers who are designing and implementing biometric systems, and practitioners who are using biometric systems in their work, such as law enforcement personnel or healthcare professionals.
Table of Contents
Preface xiii
1 Multimodal Biometric in Computer Vision 1
Sunayana Kundan Shivthare, Yogesh Kumar Sharma and Ranjit D. Patil
1.1 Introduction 2
1.2 Importance of Artificial Intelligence, Machine Learning and Deep Learning in Biometric System 2
1.3 Machine Learning 4
1.4 Deep Learning 6
1.5.1 Discussions 11
1.6 Biometric System 11
1.7 Need for Multimodal Biometric 15
1.8 Databases Used by Biometric System 17
1.9 Impact of DL in the Current Scenario 19
1.10 Conclusion 24
2 A Vaccine Slot Tracker Model Using Fuzzy Logic for Providing Quality of Service 31
Mohammad Faiz, Nausheen Fatima and Ramandeep Sandhu
2.1 Introduction 32
2.2 Related Research 33
2.3 Novelty of the Proposed Work 37
2.4 Proposed Model 38
2.5 Proposed Fuzzy-Based Vaccine Slot Tracker Model 42
2.6 Simulation 44
2.7 Conclusion 47
2.8 Future Work 50
3 Enhanced Text Mining Approach for Better Ranking System of Customer Reviews 53
Ramandeep Sandhu, Amritpal Singh, Mohammad Faiz, Harpreet Kaur and Sunny Thukral
3.1 Introduction 53
3.2 Techniques of Text Mining 55
3.3 Related Research 58
3.4 Research Methodology 63
3.5 Conclusion 67
4 Spatial Analysis of Carbon Sequestration Mapping Using Remote Sensing and Satellite Image Processing 71
Prashantkumar B. Sathvara, J. Anuradha, R. Sanjeevi, Sandeep Tripathi and Ankitkumar B. Rathod
4.1 Introduction 72
4.2 Materials and Methods 75
4.3 Results 77
4.4 Conclusion 79
5 Applications of Multimodal Biometric Technology 85
Shivalika Goyal and Amit Laddi
5.1 Introduction 85
5.2 Components of MBS 87
5.3 Biometrics Modalities 89
5.4 Applications of Multimodal Biometric Systems 89
5.5 Conclusion 97
6 A Study of Multimodal Colearning, Application in Biometrics and Authentication 103
Sandhya Avasthi, Tanushree Sanwal, Ayushi Prakash and Suman Lata Tripathi
6.1 Introduction 104
6.2 Multimodal Deep Learning Methods and Applications 108
6.3 MMDL Application in Biometric Monitoring 113
6.4 Fusion Levels in Multimodal Biometrics 116
6.5 Authentication in Mobile Devices Using Multimodal Biometrics 119
6.6 Challenges and Open Research Problems 122
6.7 Conclusion 123
7 A Structured Review on Virtual Reality Technology Application in the Field of Sports 129
Harmanpreet Kaur, Arpit Kulshreshtha and Deepika Ghai
7.1 Introduction 130
7.2 Related Work 132
7.3 Conclusion 142
8 A Systematic and Structured Review of Fuzzy Logic-Based Evaluation in Sports 145
Harmanpreet Kaur, Sourabh Chhatiye and Jimmy Singla
8.1 Introduction 146
8.2 Related Works 148
8.3 Conclusion 159
9 Machine Learning and Deep Learning for Multimodal Biometrics 163
Danvir Mandal and Shyam Sundar Pattnaik
9.1 Introduction 163
9.2 Machine Learning Using Multimodal Biometrics 165
9.3 Deep Learning Using Multimodal Biometrics 167
9.4 Conclusion 169
10 Machine Learning and Deep Learning: Classification and Regression Problems, Recurrent Neural Networks, Convolutional Neural Networks 173
R. K. Jeyachitra and Manochandar, S.
10.1 Introduction 174
10.2 Classification of Machine Learning 174
10.3 Supervised Learning 175
10.4 Unsupervised Learning 201
10.5 Reinforcement Learning 203
10.6 Hybrid Approach 204
10.7 Other Common Approaches 205
10.8 DL Techniques 210
10.9 Conclusion 219
11 Handwriting and Speech-Based Secured Multimodal Biometrics Identification Technique 227
Swathi Gowroju, V. Swathi and Ankita Tiwari
11.1 Introduction 228
11.2 Literature Survey 230
11.3 Proposed Method 231
11.4 Results and Discussion 237
11.5 Conclusion 248
12 Convolutional Neural Network Approach for Multimodal Biometric Recognition System for Banking Sector on Fusion of Face and Finger 251
Sandeep Kumar, Shilpa Choudhary, Swathi Gowroju and Abhishek Bhola
12.1 Introduction 252
12.2 Literature Work 253
12.3 Proposed Work 256
12.4 Results and Discussion 260
12.5 Conclusion 265
13 Secured Automated Certificate Creation Based on Multimodal Biometric Verification 269
Shilpa Choudhary, Sandeep Kumar, Monali Gulhane and Munish Kumar
13.1 Introduction 270
13.2 Literature Work 274
13.3 Proposed Work 276
13.4 Experiment Result 278
13.5 Conclusion and Future Scope 279
14 Face and Iris-Based Secured Authorization Model Using CNN 283
Munish Kumar, Abhishek Bhola, Ankita Tiwari and Monali Gulhane
14.1 Introduction 284
14.2 Related Work 285
14.3 Proposed Methodology 287
14.4 Results and Discussion 291
14.5 Conclusion and Future Scope 296
References 297
Index 301
