End-to-end deep multi-modal physiological authentication with smartbands

Abstract

The number of fitness tracker users increases every day. Most of the applications require authentication to protect privacy-preserving operations. Biometrics such as face images have been used widely as login tokens, but they have privacy issues. Moreover, occlusions like face masks used for COVID may reduce their effectiveness. Smartbands can track heart rate, movements, and electrodermal activities. They have been widely used for health-related applications. The use of smartbands for authentication is in the exploratory stage. Physiological signals gathered from smartbands may be used to create a multi-modal and multi-sensor authentication system. The popularity of smartbands enables us to deploy new applications without a need to buy additional hardware. In this study, we explore the multi-modal physiological biometrics with end-to-end deep learning and feature-based traditional systems. We collected multi-modal physiological data of 80 people for five days using modern smartbands. We applied a deep learning approach to the multi-modal physiological data and used feature-based traditional machine learning classifiers. The CNN-LSTM model achieved a 9.31% equal error rate and outperformed other models in terms of authentication performance.