融合持续注视偏移提升眼动生物特征认证精度
Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion
- 引入连续注视偏移信息，与原有眼动特征融合增强辨识度
- 非线性融合在两个数据集上均显著提升认证准确率
- 多任务融合效果更优，适合噪声环境下的身份验证
眼动生物特征（EMB）利用个体特有的注视动态进行用户认证与识别。近年来基于深度学习的EMB系统通过建模时序眼动行为取得良好性能，但通常忽略连续注视偏移，尽管已有研究表明其包含用户区分性信息。本文探究将连续注视偏移与现有生物特征融合是否可提升生物特征表现。我们在两个公开数据集上评估了线性和非线性融合方法，数据由实验室级眼动仪和虚拟现实头显在多种任务和观测时长下采集。结果表明，融合策略在两个数据集上均有性能提升，尤其在采用非线性融合时效果更显著；跨任务融合进一步改善了认证表现。研究支持连续注视偏移可在眼动追踪质量下降或存在噪声时作为有效辅助信息。
原文摘要 · Abstract (English)
Eye movement biometrics (EMB) use subject-specific gaze dynamics for user authentication and identification. Recent deep learning-based EMB systems achieve strong performance by modeling temporal eye movement behavior. However, these systems typically overlook continuous gaze offset, despite prior evidence that it contains user-discriminative information. This work examines whether continuous gaze offset can improve biometric performance when combined with existing biometric features. We evaluate linear and nonlinear fusion methods on two publicly available datasets, collected via the lab-grade eye tracker and virtual reality headset across multiple tasks and observation durations. Results indicate that fusion offers performance benefits on both datasets, particularly when using nonlinear fusion. Additionally, fusing biometric information across multiple tasks further improves authentication performance. These findings support the hypothesis that continuous gaze offset may serve as useful auxiliary information under conditions of degraded or noisy eye tracking.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。