Feature-Guided Zero-Shot Learning for Handwriting Verification Using Inertial Sensors

Yifeng Wang, Jiangtao Xu, Yi Zhao

Abstract: In an increasingly digital world, there is an unprecedented demand in intelligent society for biometric recognition systems that balance security with convenience. Traditional methods, such as passwords and PINs, are vulnerable to breaches and impose the burden of remembering multiple credentials. To this end, we propose a handwriting verification technology leveraging inertial sensors embedded in wearable devices (e.g., smartphones). Handwriting biometrics offer enhanced security as unique writing patterns are inherently resistant to replication and forgery. However, this approach faces two critical challenges: Identity verification independent of written content, and generalizability to unseen writers during deployment. We therefore devise a feature-guided zero-shot learning (FGZSL) framework, which constructs a unique feature vector for each sample and then performs identity verification by comparing these feature vectors. For the first challenge, we design an aim focuser, a module that filters out irrelevant content from the data features, allowing the FGZSL to focus on identity-specific information rather than writing content. For the second challenge, we design a plug-and-play Rényi-entropy-based representation regularization, which constructs informative and discriminative features for seen categories during training. These features serve as bases for representing unseen categories during testing. We contribute the first inertial identity detection dataset, publicly available on GitHub, containing 39 800 training samples and 10 000 test samples. Extensive experiments demonstrate that the FGZSL framework outperforms existing methods in both seen and unseen categories, but also sets a new standard for secure and reliable identity authentication using inertial sensors.

External IDs:dblp:journals/tii/WangXZ25