论文标题
闭塞 - 自适应深网，用于稳健的面部表情识别
Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition
论文作者
论文摘要
认识到部分遮挡的面孔的表达是一个具有挑战性的计算机视觉问题。先前的表达识别方法，忽略了此问题，或使用极端假设解决了该问题。由于人类视觉系统擅长忽略阻塞并专注于非封闭面部面积的事实，我们提出了一个具有里程碑意义的引导注意分支，以查找并丢弃封闭区域的损坏特征，以便它们不用于识别。首先生成注意力图以指示是否遮住了特定的面部部分，并指导我们的模型参加非封闭区域。为了进一步提高鲁棒性，我们提出了一个面部区域分支，将特征图划分为非重叠的面部块和任务每个块以独立预测表达式。这会导致更多样化和歧视性的特征，即使面部被部分遮挡，也可以使表达识别系统恢复。根据两个分支的协同效应，我们的闭合 - 自适应深网在两个具有挑战性的内部基准数据集和三个现实世界中的闭塞表达数据集方面显着优于最先进的方法。
Recognizing the expressions of partially occluded faces is a challenging computer vision problem. Previous expression recognition methods, either overlooked this issue or resolved it using extreme assumptions. Motivated by the fact that the human visual system is adept at ignoring the occlusion and focus on non-occluded facial areas, we propose a landmark-guided attention branch to find and discard corrupted features from occluded regions so that they are not used for recognition. An attention map is first generated to indicate if a specific facial part is occluded and guide our model to attend to non-occluded regions. To further improve robustness, we propose a facial region branch to partition the feature maps into non-overlapping facial blocks and task each block to predict the expression independently. This results in more diverse and discriminative features, enabling the expression recognition system to recover even though the face is partially occluded. Depending on the synergistic effects of the two branches, our occlusion-adaptive deep network significantly outperforms state-of-the-art methods on two challenging in-the-wild benchmark datasets and three real-world occluded expression datasets.
