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首页> 外文会议>2018 13th IEEE International Conference on Automatic Face amp; Gesture Recognition >Unsupervised Domain Adaptation with Regularized Optimal Transport for Multimodal 2D+3D Facial Expression Recognition
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Unsupervised Domain Adaptation with Regularized Optimal Transport for Multimodal 2D+3D Facial Expression Recognition

机译：具有多模式2D + 3D面部表情识别的正则化优化运输的无监督域自适应

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摘要

Since human expressions have strong flexibility and personality, subject-independent facial expression recognition is a typical data bias problem. To address this problem, we propose a novel approach, namely unsupervised domain adaptation with regularized optimal transport for multimodal 2D+3D Facial Expression Recognition (FER). In particular, Wasserstein distance is employed to measure the distribution inconsistency between the training samples (i.e. source domain) and test samples (i.e. target domain). Minimization of this Wasserstein distance is equivalent to finding an optimal transport mapping from training to test samples. Once we find this mapping, original training samples can be transformed into a new space in which the distributions of the mapped training samples and the test samples can be well-aligned. In this case, classifier learned from the transformed training samples can be well generalized to the test samples for expression prediction. In practice, approximate optimal transport can be effectively solved by adding entropy regularization. To fully explore the class label information of training samples, group sparsity regularizer is also used to enforce that the training samples from the same expression class can be mapped to the same group. Experimental results evaluated on the BU-3DFE and Bosphorus databases demonstrate that the proposed approach can achieve superior performance compared with the state-of-the-art methods.
机译：由于人类表情具有很强的灵活性和个性，因此独立于主题的面部表情识别是一个典型的数据偏差问题。为了解决这个问题，我们提出了一种新颖的方法，即用于多模式2D + 3D面部表情识别（FER）的具有正则化最优传输的无监督域自适应。特别地，使用Wasserstein距离来测量训练样本（即，源域）和测试样本（即，目标域）之间的分布不一致。将此Wasserstein距离最小化等效于找到从训练到测试样本的最佳传输映射。一旦找到此映射，就可以将原始训练样本转换为新的空间，在该空间中可以很好地对齐映射的训练样本和测试样本的分布。在这种情况下，从转换后的训练样本中学到的分类器可以很好地推广到测试样本以进行表达预测。在实践中，可以通过添加熵正则化来有效解决近似最优输运。为了充分探索训练样本的类别标签信息，还使用组稀疏性正则化器来强制将来自同一表达类别的训练样本映射到同一组。在BU-3DFE和Bosphorus数据库上评估的实验结果表明，与最新方法相比，该方法可实现更高的性能。

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