Spatio-Temporal Dual-Attention Transformer for Time-Series Behavioral Biometrics

Abstract

Continuous Authentication (CA) using behavioral biometrics is a type of biometric identification that recognizes individuals based on their unique behavioral characteristics. Many behavioral biometrics can be captured through multiple sensors, each providing multichannel time-series data. Utilizing this multichannel data effectively can enhance the accuracy of behavioral biometrics-based CA. This paper extends BehaveFormer, a new framework that effectively combines time series data from multiple sensors to provide higher security in behavioral biometrics. BehaveFormer includes two Spatio-Temporal Dual Attention Transformers (STDAT), a novel transformer we introduce to extract more discriminative features from multichannel time-series data. Experimental results on two behavioral biometrics, Keystroke Dynamics and Swipe Dynamics with Inertial Measurement Unit (IMU), have shown State-of-the-art performance. For Keystroke, on three publicly available datasets (Aalto DB, HMOG DB, and HuMIdb), BehaveFormer outperforms the SOTA. For instance, BehaveFormer achieved an EER of 2.95% on the HuMIdb. For Swipe, on two publicly available datasets (HuMIdb and FETA) BehaveFormer outperforms the SOTA, for instance, BehaveFormer achieved an EER of 3.67% on the HuMIdb. Additionally, the BehaveFormer model shows superior performance in various CA-specific evaluation metrics. The proposed STDAT-based BehaveFormer architecture can also be effectively used for transfer learning. The model weights and reproducible experimental results are available at: https://github.com/nganntk/BehaveFormer