A Wrist-Rolling Motion Recognition Method for Mobile Phone Unlocking

Recently, secure and reliable identity recognition in mobile phones has become critical due to the rising cyber threats. Traditional identity recognition methods, such as passwords, are prone to brute-force attacks and phishing, while the existing biometric recognition, such as face recognition, voice recognition, and fingerprint recognition, could be confronted with the issues of leakage and variability of the identity features, making them easy to replicate or degrade over time due to varying ages and environmental factors. To this end, we propose the wrist-rolling motion recognition (WRMR) method. WRMR leverages the unique motion patterns that require the sensors embedded in mobile phones. These motion patterns rely on the complex muscle memory and coordination, which makes the motion patterns highly individual and difficult to imitate, reducing the risk of the identity features leakage. Moreover, the wrist-rolling motion patterns are less affected by the ages and emotional states of users, ensuring the long-term consistency and reliability. In WRMR, we specially introduce a Transformer-based model, termed patch time series transformer with the temporal-spatio attention (PatchTST-TSA), which accurately classifies the time series data generated by the wrist-rolling motion. PatchTST-TSA enhances the original Transformer model structure by incorporating the patch embedding and temporal-spatio attention (TSA), thus effectively capturing the local dependencies and extracting the temporal-spatio features. Extensive experiments demonstrate that PatchTST-TSA significantly improves the classification performance and noise resilience. WRMR presents a robust and accurate solution for the mobile phone unlocking, and highlights the potential of the time series classification in the identity recognition.