Startseite Mathematik Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning
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Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning

  • K. Jothimani , S. Thangamani , D. Kiruthika und R. Saranya
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Cybersecurity Unlocked
Ein Kapitel aus dem Buch Cybersecurity Unlocked

Abstract

Biometric authentication is a pivotal technology for ensuring seamless, secure, and user-friendly access control for mobile devices. This chapter provides three biometric methods such as face recognition, fingerprint recognition, and iris scanning. Each of these technologies is evaluated in terms of accuracy, technical mechanisms, reliability, and security. Fingerprint recognition is analyzed for its simplicity and widespread compatibility with mobile hardware. Face recognition is enhanced by advancements in artificial intelligence, machine learning, and deep learning for its growing ubiquity and environmental factors. Iris scanning is recognized for its superior accuracy and resistance to forgery in the context of its limited adoption. This study further delves into the comparative strengths and weaknesses of these methods, addressing critical concerns such as spoof resistance, user privacy, and integration into multi-factor authentication systems. These recent biometric techniques are used in a variety of applications. The chapter also highlights emerging trends, such as multimodal biometric systems and AI-driven enhancements, which aim to bolster the security and usability of these technologies. By providing a comprehensive analysis, this research contributes valuable insights into the evolving landscape of biometric authentication, its current applications, and future potential in enhancing mobile device security.

Abstract

Biometric authentication is a pivotal technology for ensuring seamless, secure, and user-friendly access control for mobile devices. This chapter provides three biometric methods such as face recognition, fingerprint recognition, and iris scanning. Each of these technologies is evaluated in terms of accuracy, technical mechanisms, reliability, and security. Fingerprint recognition is analyzed for its simplicity and widespread compatibility with mobile hardware. Face recognition is enhanced by advancements in artificial intelligence, machine learning, and deep learning for its growing ubiquity and environmental factors. Iris scanning is recognized for its superior accuracy and resistance to forgery in the context of its limited adoption. This study further delves into the comparative strengths and weaknesses of these methods, addressing critical concerns such as spoof resistance, user privacy, and integration into multi-factor authentication systems. These recent biometric techniques are used in a variety of applications. The chapter also highlights emerging trends, such as multimodal biometric systems and AI-driven enhancements, which aim to bolster the security and usability of these technologies. By providing a comprehensive analysis, this research contributes valuable insights into the evolving landscape of biometric authentication, its current applications, and future potential in enhancing mobile device security.

Kapitel in diesem Buch

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
https://doi.org/10.1515/9783111712895-015

Kapitel in diesem Buch

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
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Heruntergeladen am 24.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/9783111712895-015/html
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