Improving Eye Movement Biometrics Using Remote Registration of Eye Blinking Patterns

随着先进技术日益融入我们的日常生活，对可靠的生物特征认证和识别系统的需求比以往任何时候都更加突出，以提高安全性和隐私性，并减少规避风险。尽管准确性很高，但目前的生物识别技术依赖于特定的物理特征，如指纹，面部和虹膜特征，很容易受到欺骗和伪造。因此，真实的需要开发用于人类识别的更可靠的生物识别解决方案，该解决方案能够连续地识别/认证人，而不需要他们的特定动作（例如，将手指放在扫描仪上或看着虹膜相机）。在最近的NSERC Engage项目中，我们 ** 进行了一项基于眼睛运动和眨眼的远程视频捕获的人类识别可行性研究。通过融合两种生物识别模式，即眼球运动（凝视）和眨眼，我们能够 ** 实现非常好的结果，高达95.83%的识别率和2.15%的错误率。数据是在 ** 多伦多大学实验室从22名观看精心挑选的视频序列的志愿者那里收集的。在这个 ** 项目中，我们将进行同样的实验，将一个摄像头安装在汽车仪表板的前面，面向 ** 司机。由于有不同的（和不断变化的）照明条件和更多的运动所做的 ** 司机，可能有更多的离群值和变化的数据，可能会改变结果。此外，它将测试 ** 理论与实践的模拟驾驶结果将对一个真实的会议。收集的数据 ** 还将决定系统在非侵入性方面的实用性，以及它可以 ** 持续验证驾驶员的频率-特别是在极端情况下。为了完成这项任务，我们计划 ** 进行实验，使用真实的生活驾驶模拟器可在我们的工业合作伙伴，ACS公司。

With increasing integration of advanced technologies into our daily life, the need of reliable biometric**authentication and identification systems is more pronounced than ever before in order to improve security and**privacy and to reduce the risk of circumvention. Despite high accuracy, current biometric technologies relying**on particular physical features such as fingerprints, face and iris characteristics are vulnerable to spoofing and**forgery. Therefore, there is a real need for development of more reliable biometric solutions for human**recognition that are able to continuously identify/authenticate people without requiring specific actions from**them (e.g. placing fingers on an scanner or looking into an iris camera). In a recent NSERC Engage project, we**carried out a feasibility study on human recognition based on remote video capture of eye movements and**blinking. By fusing two biometric modalities that is eye movement (gaze) and eye blinking we were able to**achieve very good results that is up to 95.83% recognition rate and 2.15% error rate. Data were collected in the**University of Toronto Laboratory from 22 volunteers watching carefully selected video sequences. In this**project we will conduct the same experiment with a camera attached to the front of a car's dashboard, facing**towards the driver. As there are different (and changing) lighting conditions and more movements done by the**driver, there may be more outliers and variation in the data that may alter the results. Furthermore, it will test**the theory with practice as the simulated driving results will be put up against a real session. The data collected**would also determine how pragmatic the system is in being non-invasive, as well as how often it can**continuously authenticate the driver- especially in extreme cases. In order to accomplish this task, we plan to**perform experiments using the real life driving simulator available at our industrial partner, ACS corp.