Identifying Who You Are No Matter What You Write Through Abstracting Handwriting Style

With the increasing use of electronic devices, online handwriting verification has become crucial for biometrics-based identity authentication. Traditional methods, which rely on content-dependent verification of the writer's name, are vulnerable to forgery. This paper introduces a content-independent handwriting authentication system, Ph-Wri, designed for commodity smartphones. The core innovation is a multi-path attention feature fusion network that combines both static features (image of the handwritten text) and dynamic features (time-dependent properties during writing), to abstract the handwriting style instead of specific content for recognition, enabling robust user authentication. To extract handwriting style from dynamic writing features, we propose a polarity-Aware attention strategy during training. This strategy incorporates Style Channel Attention (SCA) to capture direction-sensitive stylistic features, and Trajectory Spatial Attention (TSA) to highlight key handwriting trajectory regions. In the fine-Tuning stage, the Correlation-Aware Attention (CAA) module models inter-channel structural correlations, mitigating the influence of content and enhancing style-consistent representations. By linking content-independent handwriting style to user identity, the system achieves accurate authentication. Extensive experiments on both the self-built CIEHD dataset and the public BiosecurID dataset demonstrate exceptional performance, achieving a 99% Verification Accuracy on CIEHD. Compared to state-of-The-Art methods that utilize only static or dynamic data, Ph-Wri significantly reduces the Equal Error Rate, showcasing the effectiveness and practicality of the proposed approach.