Abstract Facial Expression Recognition (FER) is a challenging yet important research topic owing to its significance with respect to its academic and commercial potentials. In this work, we propose an oriented attention pseudo-siamese network that takes advantage of global and local facial information for high accurate FER. Our network consists of two branches, a maintenance branch that consisted of several convolutional blocks to take advantage of high-level semantic features, and an attention branch that possesses a UNet-like architecture to obtain local highlight information. Specifically, we first input the face image into the maintenance branch. For the attention branch, we calculate the correlation coefficient between a face and its sub-regions. Next, we construct a weighted mask by correlating the facial landmarks and the correlation coefficients. Then, the weighted mask is sent to the attention branch. Finally, the two branches are fused to output the classification results. As such, a direction-dependent attention mechanism is established to remedy the limitation of insufficient utilization of local information. With the help of our attention mechanism, our network not only grabs a global picture but can also concentrate on important local areas. Experiments are carried out on 4 leading facial expression datasets. Our method has achieved a very appealing performance compared to other state-of-the-art methods.

中文翻译：

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OAENet：用于准确面部表情识别的定向注意力集成

摘要 面部表情识别（FER）因其在学术和商业潜力方面的重要性而成为一个具有挑战性但又很重要的研究课题。在这项工作中，我们提出了一种利用全局和局部面部信息来实现高精度 FER 的定向注意伪连体网络。我们的网络由两个分支组成，一个由几个卷积块组成的维护分支，以利​​用高级语义特征，以及一个具有类似 UNet 架构的注意力分支，以获取局部高光信息。具体来说，我们首先将人脸图像输入到维护分支中。对于注意力分支，我们计算人脸与其子区域之间的相关系数。下一个，我们通过关联面部标志和相关系数来构建加权掩码。然后，加权掩码被发送到注意分支。最后将两个分支融合输出分类结果。因此，建立了依赖于方向的注意力机制来弥补局部信息利用不足的局限性。借助我们的注意力机制，我们的网络不仅可以抓取全局图片，还可以专注于重要的局部区域。在 4 个领先的面部表情数据集上进行了实验。与其他最先进的方法相比，我们的方法取得了非常吸引人的性能。建立了方向依赖的注意力机制来弥补局部信息利用不足的局限性。借助我们的注意力机制，我们的网络不仅可以抓取全局图片，还可以专注于重要的局部区域。在 4 个领先的面部表情数据集上进行了实验。与其他最先进的方法相比，我们的方法取得了非常吸引人的性能。建立了方向依赖的注意力机制来弥补局部信息利用不足的局限性。借助我们的注意力机制，我们的网络不仅可以抓取全局图片，还可以专注于重要的局部区域。在 4 个领先的面部表情数据集上进行了实验。与其他最先进的方法相比，我们的方法取得了非常吸引人的性能。