通过击键动力学和基于手套的手部运动学融合的多模态用户认证方法
Multimodal User Authentication Method via Fusion of Keystroke Dynamics and Glove-Based Hand Kinematics
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中文总结 AI 辅助
研究针对击键动力学受环境影响问题，提出融合击键动力学与手部运动学的多模态认证框架，用定制手套捕获特征，经混合架构融合，通过“未见”评估协议验证，该方法能有效提升认证准确率，融合模态增强了生物识别辨别力。
中文摘要 AI 辅助
尽管击键动力学是具有成本效益的行为生物识别技术，但其实际部署受到环境变化影响的阻碍。为解决此问题，我们提出了一个强大的多模态认证框架，使用19维手部运动学增强传统击键动力学。通过配备10个压阻式压力传感器和一个9轴惯性测量单元（IMU）的定制数据手套捕获特征。采用混合CNN-LSTM架构有效融合这些异构时间序列流。为确保实际适用性，实施了严格的“未见”评估协议：模型在桌面键盘上使用1个目标用户和8个“已知”冒名顶替者的数据进行训练，但在笔记本电脑键盘上（跨域）针对目标和训练中排除的“未知”冒名顶替者进行评估。在五次试验中平均，多模态方法在单个600毫秒认证事件（窗口1）中实现了2.12%的平均等错误率（EER）。关键的是，使用时间平滑窗口在10个事件（6秒）上聚合分数可实现完美认证（0.00% EER）。虽然我们的消融研究表明仅IMU运动学在静态实验室中可实现等效性能，但错误分析证实压力和IMU传感器尽管有很强的物理相关性，但对错误因素具有不同的敏感性。融合这些模态建立了针对空间欺骗和环境噪声等现实世界漏洞的重要故障安全机制，突出表明组合的物理特征比单独的时间特征提供更强的生物识别辨别力。
英文摘要
Although keystroke dynamics are cost-effective behavioral biometrics, their practical deployment is hindered by susceptibility to environmental variations. To address this, we propose a robust multimodal authentication framework that augments traditional keystroke dynamics using 19-dimensional hand kinematics. Features are captured using a bespoke data glove equipped with 10 piezoresistive pressure sensors and a 9-axis IMU. A hybrid CNN-LSTM architecture effectively fuses these heterogeneous time-series streams. To ensure real-world applicability, we implemented a rigorous "unseen" evaluation protocol: the model was trained on a desktop keyboard using data from 1 target user and 8 "known" impostors, but evaluated on a laptop keyboard (cross-domain) against the target and an "unknown" impostor excluded from training. Averaged over five trials, the multimodal method achieved a mean Equal Error Rate (EER) of 2.12% for individual 600-ms authentication events (Window 1). Crucially, aggregating scores over 10 events (6 seconds) using a temporal smoothing window yielded perfect authentication (0.00% EER). While our ablation study showed IMU kinematics alone achieved equivalent performance in a static laboratory, error analysis confirmed that pressure and IMU sensors, despite strong physical correlation, possess distinct sensitivities to error factors. Fusing these modalities establishes a vital fail-safe against real-world vulnerabilities like spatial spoofing and environmental noise, highlighting that combined physical traits provide much stronger biometric discrimination than timing features alone.
