Emotion recognition from facial expressions has gained much interest over the last few decades. In the literature, the common approach, used for facial emotion recognition (FER), consists of these steps: image pre-processing, face detection, facial feature extraction, and facial expression classification (recognition). We have developed a method for FER that is absolutely different from this common approach. Our method is based on the dimensional model of emotions as well as on using the kriging predictor of Fractional Brownian Vector Field. The classification problem, related to the recognition of facial emotions, is formulated and solved. The relationship of different emotions is estimated by expert psychologists by putting different emotions as the points on the plane. The goal is to get an estimate of a new picture emotion on the plane by kriging and determine which emotion, identified by psychologists, is the closest one. Seven basic emotions (Joy, Sadness, Surprise, Disgust, Anger, Fear, and Neutral) have been chosen. The accuracy of classification into seven classes has been obtained approximately 50%, if we make a decision on the basis of the closest basic emotion. It has been ascertained that the kriging predictor is suitable for facial emotion recognition in the case of small sets of pictures. More sophisticated classification strategies may increase the accuracy, when grouping of the basic emotions is applied. PDF  XML

中文翻译：

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使用人类情绪的数值接近度来进行人脸情绪识别的Kriging预测器

在过去的几十年中，面部表情对情感的识别引起了人们的极大兴趣。在文献中，用于面部情感识别（FER）的通用方法包括以下步骤：图像预处理，面部检测，面部特征提取和面部表情分类（识别）。我们已经开发出一种与这种通用方法完全不同的FER方法。我们的方法基于情感的维度模型以及使用分数布朗矢量场的克里金预测因子。提出并解决了与面部表情识别有关的分类问题。专家心理学家通过将不同的情感作为平面上的点来估计不同情感的关系。目的是通过克里金法对飞机上的新图画情绪进行估计，并确定由心理学家确定的哪种情绪最接近。七种基本情绪（选择了“欢乐”，“悲伤”，“惊喜”，“厌恶”，“愤怒”，“恐惧”和“中立”。如果我们根据最接近的基本情感做出决定，则可以将分类为7类的准确性提高到大约50％。已经确定的是，在小图片集的情况下，克里金预测器适合面部表情识别。当应用基本情感分组时，更复杂的分类策略可以提高准确性。PDF XML