Edge Convolutional Network for Facial Action Intensity Estimation
Edge Convolutional Network for Facial Action Intensity Estimation
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DOI：
10.1109/fg.2018.00034
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发表时间：
2018-05
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影响因子：
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通讯作者：
Liandong Li;T. Baltrušaitis;Bo Sun;Louis-Philippe Morency
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文献类型：
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作者：
Liandong Li;T. Baltrušaitis;Bo Sun;Louis-Philippe Morency
在本文中，我们提出了一种新的卷积神经结构的面部动作单元强度估计。虽然卷积神经网络（CNN）在广泛的计算机视觉任务中表现出了巨大的潜力，但这些成就并没有转化为面部表情分析，手工制作的功能（例如定向梯度直方图）仍然非常具有竞争力。我们介绍了一种新的边缘卷积网络（ECN），能够捕捉面部外观的细微变化。我们的模型能够学习边缘检测器，可以在多个方向和频率上捕捉细微的皱纹和面部肌肉轮廓。我们的ECN模型的核心新奇在于其第一层，它集成了三个主要组件：边缘滤波器生成器，接收门和滤波器旋转器。所有组件都是可区分的，我们的ECN模型是端到端可训练的，并学习面部表情分析的重要边缘检测器。两个面部动作单元数据集上的实验表明，所提出的ECN优于国家的最先进的方法，这两个Au强度估计任务。
In this paper, we propose a novel convolutional neural architecture for facial action unit intensity estimation. While Convolutional Neural Networks (CNNs) have shown great promise in a wide range of computer vision tasks, these achievements have not translated as well to facial expression analysis, with hand crafted features (e.g. the Histogram of Orientated Gradient) still being very competitive. We introduce a novel Edge Convolutional Network (ECN) that is able to capture subtle changes in facial appearance. Our model is able to learn edge-like detectors that can capture subtle wrinkles and facial muscle contours at multiple orientations and frequencies. The core novelty of our ECN model is in its first layer which integrates three main components: an edge filter generator, a receptive gate and a filter rotator. All the components are differentiable and our ECN model is end-to-end trainable and learns the important edge detectors for facial expression analysis. Experiments on two facial action unit datasets show that the proposed ECN outperforms state-of-the-art methods for both AU intensity estimation tasks.