To address the performance issues of signature anti-counterfeiting verification in packaging, the work aims to propose and validate a mechanical feature with high discriminative capability under purely mechanical driving conditions, thereby enhancing the security and reliability of packaging authentication and responsibility tracing. On the basis of reviewing commonly used mechanical features in existing research, four types of candidate features were constructed by integrating a simplified dynamic model, including normal force, the ratio of primary to auxiliary directional force components, force integral, and force differential over the entire writing process. A ranking-based feature selection method was employed for screening. Signature data was collected with a self-developed digital stylus pen, capable of simultaneously recording three-dimensional force, azimuth angle, and barrel rotation angle. A comparative analysis with methods like dynamic time warping revealed that the mechanical feature-based method significantly outperformed the baseline methods in metrics such as equal error rate and classification accuracy, validating the effectiveness of the proposed feature set. The selected dynamic features provide a basis for enhancing the performance of mechanically-driven signature verification systems and offer valuable insights for future improvements to dynamic models and system optimization in packaging scenarios.
Key words
signature verification /
feature extraction /
dynamical model
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