Research of autoencoder-based user biometric verification with motion patterns

Abstract

In the current research, we continue our previous study regarding motion-based user biometric verification, which consumes sensory data. Sensory-based verification systems empower the continuous authentication narrative – as physiological biometric methods mainly based on photo or video input meet a lot of difficulties in implementation. The research aims to analyze how various components of sensor data from an accelerometer affect and contribute to defining the process of unique person motion patterns and understanding how it may express the human behavioral patterns with different activity types. The study used the recurrent long-short-term-memory autoencoder as a baseline model. The choice of model was based on our previous research. The research results have shown that various data components contribute differently to the verification process depending on the type of activity. However, we conclude that a single sensor data source may not be enough for a robust authentication system. The multimodal authentication system should be proposed to utilize and aggregate the input streams from multiple sensors as further research.

Research on hybrid transformer-based autoencoders for user biometric verification

“…The overall model architecture used for such tasks is autoencoder. We have reviewed and experimented with the usage of autoencoders for biometric verification tasks in our previous research [10]. In another paper, we reviewed which sensor data signal contributes the most to creating a distinctive user pattern [11].…”