OCA3D: Occlusion-Cause-Aware 3D Facial Evidence Completion for Facial Expression Recognition

Real-world facial occlusion arises from two fundamentally different causes: external objects, such as masks, glasses, hair, and hands, and pose-induced self-occlusion. Existing methods typically address external occlusion and self-occlusion either independently or as generic appearance corruption, without explicitly distinguishing their underlying visibility mechanisms or exploiting cause-specific 3D facial correspondences for evidence recovery. We propose OCA3D, an occlusion-cause-aware 3D facial evidence completion framework that explicitly models these two sources of missing facial evidence. From each input image, OCA3D predicts an external-occlusion mask and regresses FLAME parameters, based on which a mesh-bound 3D Gaussian representation is constructed over the fixed facial topology. Self-occluded regions are completed through topology-defined bilateral symmetry, while Gaussian attributes for externally occluded regions are recovered from reliable visible mesh faces through nearest-neighbor correspondence within fine-grained semantic regions. The completed representation is rendered from both the input and frontal views, and the resulting evidence is fused with original-image features for FER. We further introduce Occlu-FER-Mask, a manually annotated dataset providing pixel-level supervision for external-occlusion segmentation in real-world FER images. OCA3D achieves Top-1 accuracies of 93.06%, 92.15%, 90.19%, and 89.73% on RAF-DB, FERPlus, Occlusion-RAF-DB, and Occlusion-FERPlus, respectively, and remains robust under large-pose self-occlusion on Pose30/45-RAF-DB and Pose30/45-FERPlus (93.50%, 93.01%, 92.85%, 92.58%) as well as real-world external occlusions. These results demonstrate the effectiveness of modeling occlusion according to its cause and recovering expression evidence directly on the 3D facial surface.