A Fuzzy Authentication System Based on Neural Network Learning and Extreme Value Statistics

Abstract

Internet-connected smart devices in, on, and around us, (e.g., embedded devices, wearable devices, and smart sensors) can collect human biometric features and facilitate identity authentication. Existing approaches are mainly based on pattern recognition and machine learning algorithms, which may not be capable of processing uncertain user information. Thus, focusing on the uncertainty of users' identity, this article proposes a fuzzy authentication system based on neural network and extreme value analysis. Specifically, we utilize biometric gait information of human body recognition. Our proposed authentication system is designed to implicitly authenticate users based on their gait, and can detect uncertain users and reject the authentication of unknown users. The performance is evaluated using an open dataset of 153 volunteers, where we manage to achieve a recognition accuracy rate of 98.4% and an error rate of unauthorized users at 6%.