The rise of the Internet and identity authentication systems has brought convenience to peopleʼ s lives but has also introduced the potential risk of privacy leaks. Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data. This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution. The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur, which leads to advantageous characteristics such as an ultra-low latency response. We first propose a set of effective biometric features describing the motion, speed, energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities. We then train the ensemble model and non-ensemble model with our NeuroBiometric dataset for biometrics authentication. The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925. The low false positive rates ( about 0.002 ) and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a userʼ s appearance. The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.

中文翻译：

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NeuroBiometric：基于眨眼的生物特征认证系统，使用基于事件的神经形态视觉传感器

互联网和身份认证系统的兴起为人们的生活带来了便利，但同时也带来了隐私泄露的潜在风险。现有的基于显式和静态功能的生物特征认证系统具有遭受模仿数据攻击的风险。这项工作提出了一个高效的生物识别系统，该系统基于瞬态眨眼信号，该信号由具有微秒级时间分辨率的神经形态视觉传感器精确捕获。神经形态视觉传感器仅在发生眨眼时才传送由眨眼引起的局部像素级变化，这会带来有利的特性，例如超低延迟响应。我们首先提出一组有效的生物特征，以描述运动，速度，基于事件密度的微秒时间分辨率，眨眼的能量和频率信号会闪烁。然后，我们使用NeuroBiometric数据集训练整体模型和非整体模型，以进行生物特征认证。实验表明，我们的系统能够以0.948的准确度对集成模型和0.925的准确度进行识别和验证。低的假阳性率（约0.002）和高度动态的功能不仅难以复制，而且避免记录用户外观的可见特征。拟议的系统为使用神经形态视觉传感器实现更安全的身份验证提供了一条新途径。然后，我们使用NeuroBiometric数据集训练整体模型和非整体模型，以进行生物特征认证。实验表明，我们的系统能够以0.948的准确度对集成模型和0.925的准确度进行识别和验证。低的假阳性率（约0.002）和高度动态的功能不仅难以复制，而且避免记录用户外观的可见特征。拟议的系统为使用神经形态视觉传感器实现更安全的身份验证提供了一条新途径。然后，我们使用NeuroBiometric数据集训练整体模型和非整体模型，以进行生物特征认证。实验表明，我们的系统能够以0.948的准确度对集成模型和0.925的准确度进行识别和验证。低的假阳性率（约0.002）和高度动态的功能不仅难以复制，而且避免记录用户外观的可见特征。拟议的系统为使用神经形态视觉传感器实现更安全的身份验证提供了一条新途径。002）和高度动态的功能不仅难以再现，而且避免记录用户外观的可见特征。拟议的系统为使用神经形态视觉传感器实现更安全的身份验证提供了一条新途径。002）和高度动态的功能不仅难以再现，而且避免记录用户外观的可见特征。拟议的系统为使用神经形态视觉传感器实现更安全的身份验证提供了一条新途径。