Abstract The three-dimensional representation of the human face has emerged as a viable and effective way to characterize the facial surface for expression classification purposes. The rapid progress in the area continually demands its up-to-date characterization to guide and support research decisions, specially for newcomer researchers. This systematic literature review focus on investigating three major aspects of 3D facial expression recognition methods: face representation, preprocessing and classification experiments. The investigation of 49 specialized studies revealed the preferential types of data and regions of interest for face representation in recent years, as well as a trend towards keypoint-independent methods. In addition, it brings to light current weaknesses regarding the report of preprocessing techniques and identifies challenges concerning the current possibility of fair comparison among multiple methods. The presented findings outline essential research decisions whose the regardful report is of great value to this research community.

中文翻译：

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3D面部表情识别方法的系统回顾

摘要 人脸的三维表示已成为一种可行且有效的方式来表征面部表面以进行表情分类。该领域的快速发展不断需要其最新的特征来指导和支持研究决策，特别是对于新来的研究人员。这篇系统的文献综述重点研究了 3D 面部表情识别方法的三个主要方面：面部表征、预处理和分类实验。对 49 项专业研究的调查揭示了近年来人脸表示的优先数据类型和感兴趣区域，以及关键点独立方法的趋势。此外，它揭示了当前关于预处理技术报告的弱点，并确定了当前在多种方法之间进行公平比较的可能性方面的挑战。所呈现的研究结果概述了重要的研究决策，其相关报告对该研究界具有重要价值。