This paper considers the automatic labeling of emotions in face images found on social media. Facial landmarks are commonly used to classify the emotions from a face image. However, it is difficult to accurately segment landmarks for some faces and for subtle emotions. Previous authors used a Gaussian prior for the refinement of landmarks, but their model often gets stuck in a local minima. Instead, the calibration of the landmarks with respect to the known emotion class label using principal component analysis is proposed in this paper. Next, the face image is generated from the landmarks using an image translation model. The proposed model is evaluated on the classification of facial expressions and also for fish identification underwater and outperforms baselines in accuracy by over \(20\%\).

本文考虑在社交媒体上发现的人脸图像中自动标记情绪。面部标志通常用于对面部图像中的情绪进行分类。但是，对于某些人脸和微妙的情绪，很难准确地分割地标。以前的作者使用高斯先验来细化地标，但他们的模型经常陷入局部最小值。相反，本文提出了使用主成分分析相对于已知情感类标签的地标校准。接下来，使用图像转换模型从地标生成人脸图像。所提出的模型在面部表情分类和水下鱼类识别方面进行了评估，并且在准确度上优于基线超过\(20\%\)。