  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.
