Chaoji Liu1, Xingqiao Liu1,*, Chong Chen2, Kang Zhou1
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 405-440, 2024, DOI:10.32604/cmes.2023.031040
Abstract Pose-invariant facial expression recognition (FER) is an active but challenging research topic in computer vision. Especially with the involvement of diverse observation angles, FER makes the training parameter models inconsistent from one view to another. This study develops a deep global multiple-scale and local patches attention (GMS-LPA) dual-branch network for pose-invariant FER to weaken the influence of pose variation and self-occlusion on recognition accuracy. In this research, the designed GMS-LPA network contains four main parts, i.e., the feature extraction module, the global multiple-scale (GMS) module, the local patches attention (LPA) module, and the model-level fusion model. The feature extraction module… More >
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