论文标题
面部动作单元使用3D面部地标进行检测
Facial Action Unit Detection using 3D Facial Landmarks
论文作者
论文摘要
在本文中，我们建议使用3D面部标记检测面部动作单元（AU）。具体而言，我们在3D面部标志上训练2D卷积神经网络（CNN），该面部标志是使用基于形状索引的统计形状模型跟踪的，用于二进制和多级AU检测。我们表明，所提出的方法能够准确地对AU的发生进行建模，因为面部标志的运动直接对应于AUS的运动。通过在3D地标上训练CNN，我们可以在两个最先进的情感数据集（即BP4D和BP4D+）上实现准确的AU检测。使用所提出的方法，我们在330,000帧上检测到多个AU，报告了与最新方法相比的结果改善。
In this paper, we propose to detect facial action units (AU) using 3D facial landmarks. Specifically, we train a 2D convolutional neural network (CNN) on 3D facial landmarks, tracked using a shape index-based statistical shape model, for binary and multi-class AU detection. We show that the proposed approach is able to accurately model AU occurrences, as the movement of the facial landmarks corresponds directly to the movement of the AUs. By training a CNN on 3D landmarks, we can achieve accurate AU detection on two state-of-the-art emotion datasets, namely BP4D and BP4D+. Using the proposed method, we detect multiple AUs on over 330,000 frames, reporting improved results over state-of-the-art methods.
