论文标题
深度度量结构化学习以识别面部表达
Deep Metric Structured Learning For Facial Expression Recognition
论文作者
论文摘要
我们提出了一个深度度量学习模型，以创建具有定义明确的结构的嵌入式子空间。引入了将高斯结构强加于输出空间的新损失函数以创建这些子空间，从而塑造了数据的分布。鉴于其简化且建立了良好的结构，具有高斯解决方案空间的混合物是有利的。它允许在各个类别的质心中快速发现类中的类，并识别平均代表。我们还提出了一种新的半监督方法来创建子类。我们说明了有关面部表达识别问题的方法，并验证了FER+，AffectNet，扩展Cohn-Kanade（CK+），BU-3DFE和JAFFE数据集的结果。我们通过实验表明，可以成功地用于各种应用程序，包括表达检索和情绪识别。
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.
