SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild

One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in this paper, we propose a self-supervised simple facial landmark encoding (SimFLE) method that can learn effective encoding of facial landmarks, which are important features for improving the performance of FER-W, without expensive labels. Specifically, we introduce novel FaceMAE module for this purpose. FaceMAE reconstructs masked facial images with elaborately designed semantic masking. Unlike previous random masking, semantic masking is conducted based on channel information processed in the backbone, so rich semantics of channels can be explored. Additionally, the semantic masking process is fully trainable, enabling FaceMAE to guide the backbone to learn spatial details and contextual properties of fine-grained facial landmarks. Experimental results on several FER-W benchmarks prove that the proposed SimFLE is superior in facial landmark localization and noticeably improved performance compared to the supervised baseline and other self-supervised methods

AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses

Facial landmark localization aims to detect the predefined points of human faces, and the topic has been rapidly improved with the recent development of neural network based methods. However, it remains a challenging task when dealing with faces in unconstrained scenarios, especially with large pose variations. In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy. To split the search space, we propose a set of anchor templates as references for regression, which well addresses the large variations of face poses. Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses. Overall, our proposed approach, named AnchorFace, obtains state-of-the-art results with extremely efficient inference speed on four challenging benchmarks, i.e. AFLW, 300W, Menpo, and WFLW dataset. Code will be available at https://github.com/nothingelse92/AnchorFace.Comment: To appear in AAAI 202