Research in facial expression recognition and emotion detection has undergone many developments recently. Human beings express different feelings via facial expressions at different circumstances. Perceiving such type of communication helps us in building a computational human cognition model. There are seven basic emotions: anger, disgust, fear, happy, sad, surprise and neutral. Researchers have stuck to those seven emotions for research into facial expression recognition and emotion detection. Apart from those basic emotions, the existing literature has defined 21 other compound emotions. However, they failed to explore some more emotions and few have been eliminated by claiming them as meaningless. This study proposes an algorithm that offers a new approach for grouping the emotion classes. In this proposed work a total of 37 emotions are identified as combined emotions out of which 16 are newly derived. These derived emotions were unnoticed by the earlier and they are existing in real-time human emotions. Along with exploring new emotions, the claim that certain existing emotions are symmetric is challenged and proved to be asymmetric in nature. This claim is validated by employing the Facial Action Coding System (FACS) and statistical analysis of combined emotions.

近年来，面部表情识别和情绪检测的研究取得了许多进展。人类在不同情况下通过面部表情表达不同的感受。感知这种类型的交流有助于我们建立一个计算人类认知模型。有七种基本情绪：愤怒、厌恶、恐惧、快乐、悲伤、惊讶和中性。研究人员一直坚持这七种情绪来研究面部表情识别和情绪检测。除了这些基本情绪外，现有文献还定义了 21 种其他复合情绪。然而，他们未能探索更多的情绪，很少有人因声称它们毫无意义而被消除。本研究提出了一种算法，该算法提供了一种对情感类别进行分组的新方法。在这项提议的工作中，共有 37 种情绪被确定为组合情绪，其中 16 种是新衍生的。这些派生的情绪在早期没有被注意到，它们存在于实时的人类情绪中。随着对新情绪的探索，某些现有情绪是对称的这一说法受到了挑战，并被证明在本质上是不对称的。通过使用面部动作编码系统 (FACS) 和综合情绪的统计分析来验证这一说法。