Contrastive Representation Learning for Expression Recognition from Masked Face Images
Contrastive Representation Learning for Expression Recognition from Masked Face Images
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DOI：
10.1145/3552463.3557020
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Fanxing Luo;Long Zhao;Yu Wang;Jien Kato
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文献类型：
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作者：
Fanxing Luo;Long Zhao;Yu Wang;Jien Kato
随着COVID-19在全球范围内的传播，人们正在尝试不同的方法来防止病毒的传播。最实用、最受欢迎的方法之一是戴口罩。大多数人外出时都戴着口罩，这使得面部表情识别变得更加困难。因此，如何提高面部表情识别模型在蒙面人脸上的性能成为一个重要的课题。然而，目前还没有包含带面具的面部表情的公共数据集。因此，我们构建了两个数据集，一个是真实世界的面具面部表情数据库（VIP-DB），另一个是人工面具面部表情数据库（M-RAF-DB）。为了减少掩模的影响，我们利用对比表征学习并提出了一个双分支网络。我们研究了对比学习对两个数据集的影响。结果表明，对比表示学习提高了蒙面人脸表情识别的性能。
With the worldwide spread of COVID-19, people are trying different ways to prevent the spread of the virus. One of the most useful and popular ways is wearing a face mask. Most people wear a face mask when they go out, which makes facial expression recognition become harder. Thus, how to improve the performance of the facial expression recognition model on masked faces is becoming an important issue. However, there is no public dataset that includes facial expressions with masks. Thus, we built two datasets which are a real-world masked facial expression database (VIP-DB) and a man-made masked facial expression database (M-RAF-DB). To reduce the influence of masks, we utilize contrastive representation learning and propose a two-branches network. We study the influence of contrastive learning on our two datasets. Results show that using contrastive representation learning improves the performance of expression recognition from masked face images.