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Multimodal Biometric and Machine Learning Technologies

Applications for Computer Vision

Edited Sandeep Kumar, Deepika Ghai, Arpit Jain, Suman Lata Tripathi and Shilpa Rani
Copyright: 2023   |   Status: Published
ISBN: 9781119785408  |  Hardcover  |  
321 pages
Price: $195 USD
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One Line Description
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.

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.

Description
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.

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Author / Editor Details
Sandeep Kumar, PhD, is a professor in Computer Science & Engineering, Koneru Lakshmaiah Educational Foundation, India, He has published more than 150 journal articles and conference papers, 20 patents, and authored 13 books.

Deepika Ghai, PhD, is an assistant professor at Lovely Professional University, India. She has published more than 35 research papers in refereed journals and conferences. She received the Dr. C.B. Gupta Award in 2021 at Lovely Professional University

Arpit Jain, PhD, is a professor at the Koneru Lakshmamai University Education Foundation, Vijayawada, A.P., India. He has published more than 40 research papers in international journals, filed 25+ patents as well as authored/edited five books.

Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 105 research papers in refereed journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.

Shilpa Rani, PhD, is an associate professor at the Neil Gogte Institute of Technology, Hyderabad, India, and specializes in computer science & engineering. She has authored seven books, more than 50 journal articles conference papers, as well as 14 patents.

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Table of Contents
Preface
1. Multimodal Biometric in Computer Vision
Sunayana Kundan Shivthare, Yogesh Kumar Sharma
and Ranjit D. Patil
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
2. A Vaccine Slot Tracker Model Using Fuzzy Logic for Providing Quality of Service
Mohammad Faiz, Nausheen Fatima and Ramandeep Sandhu
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
3. Enhanced Text Mining Approach for Better Ranking System of Customer Reviews
Ramandeep Sandhu, Amritpal Singh, Mohammad Faiz, Harpreet Kaur and Sunny Thukral
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
4. Spatial Analysis of Carbon Sequestration Mapping Using Remote Sensing and Satellite Image Processing
Prashantkumar B. Sathvara, J. Anuradha, R. Sanjeevi, Sandeep Tripathi and Ankitkumar B. Rathod
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
5. Applications of Multimodal Biometric Technology
Shivalika Goyal and Amit Laddi
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
6. A Study of Multimodal Colearning, Application in Biometrics and Authentication
Sandhya Avasthi, Tanushree Sanwal, Ayushi Prakash and Suman Lata Tripathi
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
7. A Structured Review on Virtual Reality Technology Application in the Field of Sports
Harmanpreet Kaur, Arpit Kulshreshtha and Deepika Ghai
7.1 Introduction
7.2 Related Work
7.3 Conclusion
References
8. A Systematic and Structured Review of Fuzzy Logic-Based Evaluation in Sports
Harmanpreet Kaur, Sourabh Chhatiye and Jimmy Singla
8.1 Introduction
8.2 Related Works
8.3 Conclusion
References
9. Machine Learning and Deep Learning for Multimodal Biometrics
Danvir Mandal and Shyam Sundar Pattnaik
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
10. Machine Learning and Deep Learning: Classification and Regression Problems, Recurrent Neural Networks, Convolutional Neural Networks
R. K. Jeyachitra and Manochandar, S.
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
11. Handwriting and Speech-Based Secured Multimodal Biometrics Identification Technique
Swathi Gowroju, V. Swathi and Ankita Tiwari
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
12. Convolutional Neural Network Approach for Multimodal Biometric Recognition System for Banking Sector on Fusion of Face and Finger
Sandeep Kumar, Shilpa Choudhary, Swathi Gowroju and Abhishek Bhola
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
13. Secured Automated Certificate Creation Based on Multimodal Biometric Verification
Shilpa Choudhary, Sandeep Kumar, Monali Gulhane and Munish Kumar
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
14. Face and Iris-Based Secured Authorization Model Using CNN
Munish Kumar, Abhishek Bhola, Ankita Tiwari and Monali Gulhane
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

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