Research Area: Machine Learning
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.
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Author(s) Name: Gilderlane Ribeiro Alexandre, José Marques Soares, George André Pereira Thé
Journal name: Pattern Recognition
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Publisher name: Elsevier
DOI: 10.1016/j.patcog.2019.107108
Volume Information: Volume 100, April 2020, 107108
