Facial action unit (AU) recognition remains a challenging task, due to the subtlety and non-rigidity of AUs. A typical solution is to localize the correlated regions of each AU. Current works often predefine the region of interest (ROI) of each AU via prior knowledge, or try to capture the ROI only by the supervision of AU recognition during training. However, the predefinition often neglects important regions, while the supervision is insufficient to precisely localize ROIs. In this paper, we propose a novel AU recognition method by prior and adaptive attention. Specifically, we predefine a mask for each AU, in which the locations farther away from the AU centers specified by prior knowledge have lower weights. A learnable parameter is adopted to control the importance of different locations. Then, we element-wise multiply the mask by a learnable attention map, and use the new attention map to extract the AU-related feature, in which AU recognition can supervise the adaptive learning of a new attention map. Experimental results show that our method (i) outperforms the state-of-the-art AU recognition approaches on challenging benchmark datasets, and (ii) can accurately reason the regional attention distribution of each AU by combining the advantages of both the predefinition and the supervision.

中文翻译：

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通过先验和自适应注意进行面部动作单元识别

由于 AU 的微妙性和非刚性，面部动作单元 (AU) 识别仍然是一项具有挑战性的任务。一个典型的解决方案是定位每个 AU 的相关区域。目前的工作通常通过先验知识预先定义每个 AU 的感兴趣区域 (ROI)，或者尝试仅通过在训练期间对 AU 识别的监督来捕获 ROI。然而，预定义经常忽略重要区域，而监督不足以精确定位 ROI。在本文中，我们通过先验和自适应注意提出了一种新颖的 AU 识别方法。具体来说，我们为每个 AU 预定义了一个掩码，其中离先验知识指定的 AU 中心较远的位置具有较低的权重。采用可学习的参数来控制不同位置的重要性。然后，我们逐元素地将掩码乘以可学习的注意力图，并使用新的注意力图提取与 AU 相关的特征，其中 AU 识别可以监督新注意力图的自适应学习。实验结果表明，我们的方法（i）在具有挑战性的基准数据集上优于最先进的 AU 识别方法，并且（ii）可以通过结合预定义和监督。