用事件相机捕捉眨眼动态，识别假脸攻击
Event-based Liveness Detection using Temporal Ocular Dynamics: An Exploratory Approach
- 通过事件相机捕捉微秒级眼动变化，分析时序特征
- 在假脸攻击下实现95.37%的识别准确率
- 适合低延迟、抗重放攻击的生物识别场景
基于RGB摄像头的活体检测在受控环境下表现良好，但在跨传感器和攻击场景下泛化能力差。本文探索事件相机作为替代感知模态，利用眼动的时序动态进行活体检测。事件相机以微秒级分辨率捕捉亮度的稀疏异步变化，可精确分析快速眼动（如扫视）。重放攻击无法真实还原这些动态，导致事件域中出现独特的时空模式。我们设计了数据采集协议，在RGBE-Gaze数据集基础上添加重放攻击样本，构建事件域的伪造样本。分析眼部区域的事件驱动时序特征，评估其在眼动分割与活体分类中的效果。结果表明，事件表示能可靠区分真实与重放序列，使用脉冲卷积神经网络最高达95.37%的准确率。初步结果表明事件传感在鲁棒、低延迟活体检测中具有潜力。
原文摘要 · Abstract (English)
Face liveness detection has been extensively studied using RGB cameras, achieving strong performance under controlled conditions but often failing to generalize across sensors and attack scenarios. In this work, we explore event cameras as an alternative sensing modality for liveness detection based on temporal ocular dynamics. Event cameras capture sparse, asynchronous changes in brightness with microsecond resolution, enabling precise analysis of fast eye movements such as saccades. Replay attacks cannot faithfully reproduce these dynamics due to temporal resampling and display artifacts, leading to distinctive spatio-temporal patterns in the event domain. We design a data collection protocol to extend RGBE-Gaze with replay-attack recordings, yielding an event-based fake counterpart for liveness detection. We analyze event-driven temporal features from eye regions and evaluate their effectiveness for ocular motion segmentation and liveness classification. Our results show that event-based representations enable reliable discrimination between genuine and replayed sequences, achieving up to 95.37% top-1 accuracy with a spiking convolutional neural network. These preliminary findings highlight the potential of event-based sensing for robust and low-latency liveness detection.
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