We propose a deep metric learning model to create embedded sub-spaces with a well defined structure. A new loss function that imposes Gaussian structures on the output space is introduced to create these sub-spaces thus shaping the distribution of the data. Having a mixture of Gaussians solution space is advantageous given its simplified and well established structure. It allows fast discovering of classes within classes and the identification of mean representatives at the centroids of individual classes. We also propose a new semi-supervised method to create sub-classes. We illustrate our methods on the facial expression recognition problem and validate results on the FER+, AffectNet, Extended Cohn-Kanade (CK+), BU-3DFE, and JAFFE datasets. We experimentally demonstrate that the learned embedding can be successfully used for various applications including expression retrieval and emotion recognition.

中文翻译：

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用于面部表情识别的深度度量结构化学习

我们提出了一个深度度量学习模型来创建具有明确定义结构的嵌入式子空间。引入了在输出空间上强加高斯结构的新损失函数来创建这些子空间，从而塑造数据的分布。鉴于其简化和完善的结构，混合高斯解空间是有利的。它允许快速发现类中的类，并在各个类的质心处识别平均代表。我们还提出了一种新的半监督方法来创建子类。我们说明了我们在面部表情识别问题上的方法，并在 FER+、AffectNet、Extended Cohn-Kanade (CK+)、BU-3DFE 和 JAFFE 数据集上验证了结果。