A Novel Multimodal Biometric Authentication System using Machine Learning and Blockchain

TL;DR

This paper presents a new multimodal biometric authentication system that combines fingerprint, face, age, and gender using machine learning and blockchain to enhance security, transparency, and user convenience.

Contribution

It introduces a novel multimodal system integrating four biometrics with machine learning and blockchain, improving security and robustness over existing methods.

Findings

System demonstrates high accuracy and robustness.

Decision Tree effectively combines biometric results.

Initial experiments show promising performance.

Abstract

Traditional authentication systems that rely on simple passwords, PIN numbers or tokens have many security issues, like easily guessed passwords, PIN numbers written on the back of cards, etc. Thus, biometric authentication methods that rely on physical and behavioural characteristics have been proposed as an alternative for those systems. In real-world applications, authentication systems that involve a single biometric faced many issues, especially lack of accuracy and noisy data, which boost the research community to create multibiometric systems that involve a variety of biometrics. Those systems provide better performance and higher accuracy compared to other authentication methods. However, most of them are inconvenient and requires complex interactions from the user. Thus, in this paper, we introduce a novel multimodal authentication system that relies on machine learning and blockchain, with the aim of providing a more secure, transparent, and convenient authentication mechanism. The proposed system combines four important biometrics, fingerprint, face, age, and gender. The supervised learning algorithm Decision Tree has been used to combine the results of the biometrics verification process and produce a confidence level related to the user. The initial experimental results show the efficiency and robustness of the proposed multimodal systems.