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Multimodal Biometric and Machine Learning Technologies de Sandeep Kumar

Multimodal Biometric and Machine Learning Technologies

Editat de Sandeep Kumar, Deepika Ghai, Arpit Jain, Suman Lata Tripathi, Shilpa Rani
en Limba Engleză Hardback – 15 noi 2023

Problema securității digitale a depășit stadiul în care parolele complexe mai pot oferi o protecție robustă împotriva furtului de identitate. În Multimodal Biometric and Machine Learning Technologies, observăm o trecere necesară de la metodele de autentificare bazate pe cunoștințe (PIN-uri) la sisteme multimodale care procesează simultan trăsături faciale, amprente și recunoaștere vocală. Această lucrare tehnică, publicată sub egida Wiley, oferă cercetătorilor și dezvoltatorilor un cadru aplicativ pentru implementarea algoritmilor de inteligență artificială în infrastructuri critice.

Structura volumului este una progresivă, debutând cu fundamentele învățării automate (Machine Learning) și a celei de profunzime (Deep Learning) aplicate în viziunea computerizată. Găsim în această carte nu doar teorie, ci și implementări specifice, precum modelul de tracker bazat pe logică fuzzy pentru servicii publice sau tehnici avansate de text mining pentru analiza recenziilor. Considerăm că forța acestui volum rezidă în capacitatea de a explica modul în care interacțiunea om-calculator poate fi simplificată fără a compromite rigoarea securității. Cititorul care a aplicat ideile din Deep Learning for Biometrics va găsi aici o completare esențială privind fuziunea datelor și impactul social al designului biometric.

Spre deosebire de Biometric Security and Privacy, care explorează confidențialitatea în contextul IoT, volumul de față se concentrează pe algoritmi noi de modelare accesibili unei arii largi de specialiști, de la forțele de ordine la profesioniștii din sănătate. Este un ghid tehnic ce prioritizează eficiența algoritmilor în scenarii de utilizare reală, oferind baze de date de referință și studii de impact asupra scenariilor actuale de securitate.

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Specificații

ISBN-13: 9781119785408
ISBN-10: 1119785405
Pagini: 336
Dimensiuni: 154 x 230 x 24 mm
Greutate: 0.58 kg
Ediția:1
Editura: Wiley
Locul publicării:Hoboken, United States

De ce să citești această carte

Această carte este indispensabilă inginerilor de sistem și dezvoltatorilor care doresc să implementeze soluții de autentificare rezistente la atacuri cibernetice. Veți câștiga o înțelegere profundă a modului în care Machine Learning și Deep Learning pot fi integrate pentru a crea sisteme biometrice multimodale. Recomandăm acest volum pentru abordarea practică a algoritmilor de fuziune a datelor, esențiali în dezvoltarea software-ului de securitate modern.

Despre autor

Volumul este coordonat de o echipă de experți condusă de Sandeep Kumar și Suman Lata Tripathi, alături de Deepika Ghai, Arpit Jain și Shilpa Rani. Editorii au o experiență vastă în cercetarea academică și industrială, specializându-se în domenii precum viziunea computerizată, sistemele VLSI și tehnologiile emergente de inteligență artificială. Expertiza lor colectivă asigură o perspectivă multidisciplinară asupra biometriei, combinând rigoarea matematică a algoritmilor cu necesitățile practice de implementare în medii digitale complexe.

Notă biografică

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.

Cuprins

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

Descriere

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.