论文标题
灰度编码光场的面部身份验证
Face Authentication from Grayscale Coded Light Field
论文作者
论文摘要
面部验证是日常系统（例如智能手机）的快速增长的身份验证工具。尽管当前的2D面部识别方法非常准确，但最近有人建议人们希望在此类解决方案中添加一个3D传感器，以使它们更可靠，更坚固，例如使用一个人的脸部使用2D打印。但是，这需要一个相对昂贵的深度传感器。为了减轻这种情况，我们提出了一个基于纤细的灰度编码光场成像的新型身份验证系统。我们提供了直接在编码图像上工作的无重建快速反动体机制。其次是一个多视图的多模式面部验证网络，该网络给出了灰度数据以及低分辨率深度地图，可为RGB案例带来竞争性结果。我们在LFW的模拟3D（RGBD）版本上演示了解决方案的有效性，该版本将公开，并通过光场计算摄像头收购了一组真实面孔。
Face verification is a fast-growing authentication tool for everyday systems, such as smartphones. While current 2D face recognition methods are very accurate, it has been suggested recently that one may wish to add a 3D sensor to such solutions to make them more reliable and robust to spoofing, e.g., using a 2D print of a person's face. Yet, this requires an additional relatively expensive depth sensor. To mitigate this, we propose a novel authentication system, based on slim grayscale coded light field imaging. We provide a reconstruction free fast anti-spoofing mechanism, working directly on the coded image. It is followed by a multi-view, multi-modal face verification network that given grayscale data together with a low-res depth map achieves competitive results to the RGB case. We demonstrate the effectiveness of our solution on a simulated 3D (RGBD) version of LFW, which will be made public, and a set of real faces acquired by a light field computational camera.
