The most discriminative expression features are mostly concentrated in local key facial regions. Thus, we propose a simple and efficient framework that can learn more discriminative expression features from scrambled facial images. Specifically, we first divide the input image into local subregions of the same size and shuffle them randomly at a certain range to obtain the damaged image to increase the difficulty of recognition. Then, the original image and the damaged image are fed to the network. A channel attention module is exploited for highlighting the effective features and suppressing irrelevant features. Simultaneously, during the reconstruction phase, a region alignment model is appended to establish the semantic correlation between each subregion, aiming at restoring the original spatial layout of local subregions in the original image. Extensive experiments on the RAF-DB and the FERPlus datasets demonstrate that our proposed method significantly outperformed state-of-the-art methods without any external facial expression pretraining.

中文翻译：

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面部表情识别的破坏与重建学习


最具辨别力的表情特征大多集中在局部关键面部区域。因此，我们提出了一个简单而有效的框架，可以从扰乱的面部图像中学习更多有辨别力的表情特征。具体来说，我们首先将输入图像划分为相同大小的局部子区域，并在一定范围内随机打乱它们，以获得受损图像以增加识别难度。然后，将原始图像和损坏的图像输入网络。利用通道注意模块来突出有效特征并抑制不相关特征。同时，在重建阶段，附加区域对齐模型来建立每个子区域之间的语义相关性，旨在恢复原始图像中局部子区域的原始空间布局。对 RAF-DB 和 FERPlus 数据集的大量实验表明，我们提出的方法在没有任何外部面部表情预训练的情况下明显优于最先进的方法。