研究人员探索了使用事件相机进行人脸活体检测，重点关注眼动动力学。与传统RGB相机不同，事件相机以高时间分辨率捕获异步亮度变化，能够精确分析扫视等眼球运动。这些相机能够区分真实和重放序列，因为重放攻击难以复制自然的眼动动力学，从而产生可检测的时空模式。该研究使用脉冲卷积神经网络实现了高达95.37%的准确率，表明其在鲁棒性和低延迟活体检测方面具有潜力。 AI
影响 事件相机为活体检测提供了一种新颖的方法，有望提高对抗欺骗攻击的安全性。
排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了一种新的人脸活体检测方法。
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arXiv:2604.26285v1 Announce Type: new Abstract: 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 cam…
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 live…