Multimodal Biometric and Machine Learning Technologies E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 429

Veröffentlichungsjahr: 2023

Beschreibung

MULTIMODAL BIOMETRIC AND MACHINE LEARNING TECHNOLOGIES

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.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

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

1.4 Deep Learning

1.5 Related Work

1.6 Biometric System

1.7 Need for Multimodal Biometric

1.8 Databases Used by Biometric System

1.9 Impact of DL in the Current Scenari

1.10 Conclusion

References

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

2.4 Proposed Model

2.5 Proposed Fuzzy-Based Vaccine Slot Tracker Model

2.6 Simulation

2.7 Conclusion

2.8 Future Work

References

3 Enhanced Text Mining Approach for Better Ranking System of Customer Reviews

3.1 Introduction

3.2 Techniques of Text Mining

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

4.1 Introduction

4.2 Materials and Methods

4.3 Results

4.4 Conclusion

Acknowledgment

References

5 Applications of Multimodal Biometric Technology

5.1 Introduction

5.2 Components of MBS

5.3 Biometrics Modalities

5.4 Applications of Multimodal Biometric Systems

5.5 Conclusion

References

6 A Study of Multimodal Colearning, Application in Biometrics and Authentication

6.1 Introduction

6.2 Multimodal Deep Learning Methods and Applications

6.3 MMDL Application in Biometric Monitoring

6.4 Fusion Levels in Multimodal Biometrics

6.5 Authentication in Mobile Devices Using Multimodal Biometrics

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

7.1 Introduction

7.2 Related Work

7.3 Conclusion

References

8 A Systematic and Structured Review of Fuzzy Logic-Based Evaluation in Sports

8.1 Introduction

8.2 Related Works

8.3 Conclusion

References

9 Machine Learning and Deep Learning for Multimodal Biometrics

9.1 Introduction

9.2 Machine Learning Using Multimodal Biometrics

9.3 Deep Learning Using Multimodal Biometrics

9.4 Conclusion

References

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

10.4 Unsupervised Learning

10.5 Reinforcement Learning

10.6 Hybrid Approach

10.7 Other Common Approaches

10.8 DL Techniques

10.9 Conclusion

Acknowledgment

References

11 Handwriting and Speech-Based Secured Multimodal Biometrics Identification Technique

11.1 Introduction

11.2 Literature Survey

11.3 Proposed Method

11.4 Results and Discussion

11.5 Conclusion

References

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

12.4 Results and Discussion

12.5 Conclusion

References

13 Secured Automated Certificate Creation Based on Multimodal Biometric Verification

13.1 Introduction

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

14.1 Introduction

14.2 Related Work

14.3 Proposed Methodology

14.4 Results and Discussion

14.5 Conclusion and Future Scope

References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Machine learning working strategy.

Figure 1.2 Workflow of deep learning.

Figure 1.3 Structure of deep learning model.

Figure 1.4 Block diagram for multimodal biometric system using fingerprint, iris image, and voice [44].

Figure 1.5 2X2 confusion matrix.

Figure 1.6 Deep learning applications.

Chapter 2

Figure 2.1 Flowchart of the proposed vaccination tracker model.

Figure 2.2 Fuzzy based multicriteria input system.

Figure 2.3 Fuzzy-based vaccination slot tracker.

Figure 2.4 Likelihood of vaccination slots with selected input parameters.

Figure 2.5 Probability of U

1

.

Figure 2.6 Probability of U

2

.

Figure 2.7 Priority of U

3

.

Figure 2.8 Proposed vaccine slot tracker.

Chapter 3

Figure 3.1 Different techniques of text mining.

Figure 3.2 Process of text mining.

Figure 3.3 Phases of text mining.

Figure 3.4 Proposed methodology.

Figure 3.5 Apache flume configuration file screenshot.

Figure 3.6 Ten most common words.

Figure 3.7 Barplot showing 20 most common words in the extracted text.

Figure 3.8 Separate bar plots (one for recommend-yes [green] and another for recommend-no [red]).

Figure 3.9 Dimension reduction technique (word clustering).

Chapter 4

Figure 4.1 Study area located for the mapping of carbon sequestration...

Figure 4.2 Satellite date for false color composition.

Figure 4.3 Changes in the amount of carbon within the premises of the research area for the time...

Figure 4.4 Changes in the amount of carbon within the premises of the research area for the time...

Chapter 5

Figure 5.1 The fusion between iris and thumb print data inputs.

Figure 5.2 Basic components of MBS.

Figure 5.3 Examples of biometric authenticators used as biometric modalities in MBS.

Figure 5.4 Commercial applications of MBS as a tool.

Chapter 6

Figure 6.1 A simple pipeline of a multimodal system.

Figure 6.2 Different applications of multimodal deep learning.

Figure 6.3 Sample examples of images and questions-answer...

Figure 6.4 A generic process of multimodal biometric system.

Figure 6.5 Fusion at the feature level.

Figure 6.6 Fusion at matching score level.

Figure 6.7 Fusion at the decision level.

Chapter 9

Figure 9.1 The block diagram of the proposed system in Aung

et al.

[17] with multimodal biometrics.

Figure 9.2 The general block diagram of the multimodal recognition system in Daas...

Figure 9.3 The basic block diagram of the multimodal biometric authentication system is presented in El-Rahiem...

Chapter 10

Figure 10.1 Classification of machine learning.

Figure 10.2 Regression learner model [38].

Figure 10.3 Linear regression model.

Figure 10.4 Linear regression curve.

Figure 10.5 Fuzzy classification model [11].

Figure 10.6 Architectureof fuzzy neural network [11].

Figure 10.7 Basic connections [45] in Bayesian Networks: (a) serial (b) diverging and (c) converging connection.

Figure 10.8 Application of Bayesian networks [46].

Figure 10.9 Decision tree.

Figure 10.10 Artificial neural network.

Figure 10.11 Logistics regression model.

Figure 10.12 Logistics regression csurve.

Figure 10.13 Comparison of linear and logistics regression curve.

Figure 10.14 Random forest classifier.

Figure 10.15 Plot for support vector machine.

Figure 10.16 Step-by-step procedure for hierarchical clustering algorithm.

Figure 10.17 Block diagram of reinforcement algorithm [50].

Figure 10.18 RNN detailed architecture.

Figure 10.19 Simple RNN.

Figure 10.20 Types of the structure of RNN.

Figure 10.21 LSTM cell.

Figure 10.22 GRU cell.

Figure 10.23 CNN flow chart.

Chapter 11

Figure 11.1 Proposed system architecture.

Figure 11.2 Distribution of data set.

Figure 11.3 Loss and accuracy plots of training and validation samples of the model for 50 epochs.

Figure 11.4 Confusion matrix for the generated model.

Figure 11.5 Plot of validation accuracy.

Figure 11.6 Sample handwriting from CEDAR data set.

Figure 11.7 Spectrogram of sample speech snippets from speech dataset.

Figure 11.8 Predicted voice amplitude.

Figure 11.9 Loss plot of the proposed model.

Figure 11.10 Accuracy of the proposed model.

Figure 11.11 Density plot for similarity of training pairs.

Chapter 12

Figure 12.1 Process of CNN.

Figure 12.2 Process of leaky ReLU.

Figure 12.3 Process of max pooling.

Figure 12.4 Process of softmax function.

Figure 12.5 Outcomes on face data set.

Figure 12.6 Outcomes on finger data set.

Chapter 13

Figure 13.1 Various biometric traits.

Figure 13.2 Flowchart of proposed methodology.

Figure 13.3 (a) An input image of face. (b) Face detector through V-J method. (c) Input image for finger. (d) Feature extractor for finger.

Figure 13.4 (a) Sample certificate. (b) After verification winner certificate.

Figure 13.5 GUI overall module for proposed methodology.

Chapter 14

Figure 14.1 Proposed methodology.

Figure 14.2 ReLU activation function.

Figure 14.3 Max pooling process.

Figure 14.4 Confusion matrix for recognition.

Figure 14.5 Accuracy comparison between the proposed and other methods.

Figure 14.6 Shows the result of the proposed method on different data sets.

List of Tables

Chapter 2

Table 2.1 Defined parameters used for simulation.

Table 2.2 Hardware and Software configurations.

Table 2.3 Input instances for vaccine slot tracking.

Chapter 3

Table 3.1 Text mining techniques with different domains.

Table 3.2 Ratio of various product reviews.

Chapter 4

Table 4.1 Carbon sequestration data concerning time and study area.

Chapter 5

Table 5.1 Type of biometric modalities with examples.

Table 5.2 Different types of biometric authenticators with explanation and examples.

Chapter 6

Table 6.1 Description of different multimodal learning applications.

Chapter 7

Table 7.1 Existing work plan for the VR training program for various sports.

Chapter 8

Table 8.1 Existing work plan for fuzzy logic-based sports evaluation.

Chapter 11

Table 11.1 Model description using CNN model.

Table 11.2 Model description using the proposed model.

Table 11.3 Bitrate and length of the dataset.

Table 11.4 Count of train validation and test values.

Table 11.5 Comparison of all methods.

Chapter 12

Table 12.1 Existing state-of-art methodology.

Table 12.2 Outcomes on face LFW data set, CMU data set.

Table 12.3 Outcomes on finger ATVS-FFp data set, FVC2006 data set.

Table 12.4 Average accuracy on a unimodal and multimodal biometric system.

Chapter 14

Table 14.1 The accuracy comparison between the proposed and other methods.

Table 14.2 Results calculated on different data sets by using the proposed method.

Guide

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

End User License Agreement

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Multimodal Biometric and Machine Learning Technologies

Applications for Computer Vision

Edited by

Sandeep Kumar

Deepika Ghai

Arpit Jain

Suman Lata Tripathi

and

Shilpa Rani

This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-78540-8

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

This book provides relevant information on multimodal biometric and machine learning technologies in order to help students, academics, and researchers from the industry who wish to know more about real-time applications. It 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 aims to explain the underlying causes of the social and organizational problems that are typically devoted to descriptions of rehabilitation methods for specific processes. Furthermore, this book describes new algorithms for modeling accessible to scientists of all varieties.

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.

Multimodal biometric technology utilizes multiple sources of information to verify an individual’s identity. For example, facial recognition technology uses unique facial features to identify an individual, while voice recognition technology uses unique voice patterns. By combining these different sources of information, multimodal biometric technology can provide a more robust and accurate identification process.

Machine learning technology is another powerful tool used in authentication systems. Machine learning algorithms are designed to learn from data and improve over time. In authentication systems, machine learning algorithms can learn to identify patterns and anomalies in user behavior, which can help to detect and prevent fraud. The combination of multimodal biometric and machine learning technologies has enabled the development of highly secure and reliable authentication systems. These systems can provide a seamless user experience while maintaining high security. For example, a user can look at their phone to unlock it without the need to enter a password or PIN. The system uses facial recognition technology to verify the user’s identity and machine learning algorithms to detect and prevent fraud.

A primary advantage of multimodal biometric and machine learning technologies is their ability to adapt to changing circumstances. For example, if a user’s face is injured or their voice varies due to illness, the system can still verify their identity using other sources of information. Machine learning algorithms can also adapt to new types of fraud and cyber-attacks, making it more difficult for hackers to bypass the system. However, there are also some challenges associated with the use of these technologies.

Privacy concerns are a significant issue, as the collection and use of biometric data can raise ethical questions. It is essential to ensure that user data is collected and stored securely and that users are fully informed about how their data is used. In addition, the accuracy of these technologies can vary depending on the quality of the data and the algorithms used. It is essential to continually improve and refine the algorithms to ensure high accuracy and reliability.

In conclusion, multimodal biometric and machine learning technologies have revolutionized the field of security and authentication. These technologies offer a more secure and reliable authentication approach while providing a seamless user experience. However, addressing privacy concerns and improving these technologies’ accuracy and reliability is essential.

Some features of multimodal biometric-based machine learning technologies include:

Improved accuracy and reliability: By combining multiple biometric modalities, multimodal biometric systems can provide higher accuracy and reliability than systems that rely on a single biometric modality. This is because the use of multiple modalities reduces the likelihood of a false match.

Enhanced security: Multimodal biometric systems can provide improved security compared to traditional authentication systems (such as passwords or PINs), as biometric traits are unique to individuals and cannot be easily replicated or stolen.

Adaptability: Multimodal biometric systems are highly adaptable and can be customized to meet the needs of various applications and user groups. For example, a system could be designed to recognize a user based on their face, voice, fingerprint, or any combination of biometric traits.

Scalability: Multimodal biometric systems can be scaled up to handle large volumes of users without compromising on accuracy or speed.

Machine learning-based: Multimodal biometric systems often use machine learning algorithms to analyze biometric data and improve the accuracy and reliability of the system over time.

User-friendly: Multimodal biometric systems are often designed to be user-friendly, requiring minimal effort on the user’s part. For example, a system could be designed to recognize a user’s face, voice, and fingerprint simultaneously without requiring the user to perform any specific actions.

We thank all contributing authors who helped us tremendously with their contributions, time, critical thoughts, and suggestions to assemble this peer-reviewed edited volume. The editors are also thankful to Scrivener Publishing and their team for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during this work.

Sandeep Kumar

Deepika Ghai

Arpit Jain

Suman Lata Tripathi

Shilpa Rani

August 2023