RaiseAuth: a novel bio-behavioral authentication method based on ultra-low-complexity movement

Authentication plays an important role in maintaining social security. Modern authentication methods often relies on mass data datasets to implement authentication by data-driven. However, an essential question still remains unclear at data level. To what extent can the authentication movement be simplified? We theoretically explain the rationality of authentication through arm movements by mathematical modeling and design the simplest scheme of the authentication movement. At the same time, we collect a small-sample multi-category dataset that compresses the authentication movement as much as possible according to the model function. On this basis, we propose a method which consists of five different cells. Each cell is matched with a custom data preprocessing module according to the structure. Four cells are composed of neural network modules based on residual blocks, and the last cell is composed of traditional machine learning algorithms. The experimental results show that arm movements can also maintain high-accuracy authentication on small-sample multi-class datasets with very simple authentication movement.