Synergistic Approaches in Multimodal Biometric Authentication with Machine Learning and Deep Learning Paradigms

Authors

  • Jaya S. Mane Symbiosis Centre for Research and Innovation (SCRI), SIT Building, Symbiosis International (Deemed University) (SIU) Lavale, Pune 412115, Maharashtra, India
  • Snehal Bhosale Department of Electronics and Tele-Communication, Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU) Lavale, Pune 412115, Maharashtra, India

Keywords:

Biometric Fusion levels, Convolutional Neural Networks, Deep Learning, Multi-Modal Biometric System

Abstract

Biometric authentication systems have become crucial for ensuring secure access to a wide range of services and resources in our digital age. Conventional user authentication methods may fall short of the requirements for safeguarding against unauthorized access during data collection, storage, and transmission. Consequently, there is a demand for the advancement of technologies that enable user authentication based on distinctive personal identifiers, specifically biometric characteristics. Common biometric modalities include face, iris scans, palm prints, fingerprints, and voices, However, there are plenty of other biometrics, including DNA, ear scans, retinal scans, stride, and even behavioral patterns. One biometric modality (uni-modal biometrics) or a combination of several modalities (multi-modal biometrics) can be used for automated person identification. This paper offers a thorough review of the literature on various fusion techniques used in multi-modal biometrics. In addition, we provide a comparative study of advanced systems on a range of criteria and end the paper with appropriate recommendations for future work.

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Published

24.03.2024

How to Cite

Mane, J. S. ., & Bhosale, S. . (2024). Synergistic Approaches in Multimodal Biometric Authentication with Machine Learning and Deep Learning Paradigms. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 587–596. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5006

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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