Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

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A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “[243] divided one facial image into several local regions by grids, then applied MSGF operations at local blocks, and finally encoded the mean intensity of each feature map.”
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017, methodology): “Some methods in the literature [8, 7, 67] also performed the cross-database experiment in the JAFFE database (training in the CK+ database and testing in the JAFFE).”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “In [101] the facial region is divided by a grid, applying a bank of Gabor filters at each cell and radially encoding the mean intensity of each feature map.”