# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 1 Introduction  
Facial expressions are among the most pervasive and significant signals to convey human emotions and intentions, and they play a pivotal role in everyday interpersonal communication. Facial expression recognition (FER) is a significant area of artificial intelligence (AI) that is drawing more research attention because of its numerous potential applications in a variety of fields, such as healthcare, driver status detection, smart classrooms, etc. [1, 2, 3, 4, 5]. While significant progress has been made in the development of FER, factors such as light, pose change, and occlusion still present challenges that limit its accuracy and robustness. Figure 1 illustrates some examples of FER with occluded faces. Facial occlusion represents a significant challenge in the domain of face analysis. It hinders the FER model's ability to fully discern the full range of facial features, a limitation that has garnered considerable attention in the field of facial identity recognition. However, this issue has been less extensively investigated in the context of FER due to the unavailability of datasets with dedicated annotation for OFER [1; 2; 5]. It is, therefore, important to address this issue in order to enhance the accuracy and robustness of FER [6]. In the initial studies on the OFER problem, the effect of partially artificially occluded faces on FER was investigated in controlled laboratory environments. [PERSON] et al. [7] examined which areas of the human face are most important for recognition by occluding crucial regions of the face. [PERSON] et al. [8] proposed the first OFER model, which recovered facial feature points. [PERSON] et al. [9] conducted a comprehensive study of OFER based on Gabor features and an analysis of human observers, which revealed that occlusion of the mouth area had a greater impact on FER performance than occlusion of the eye area on the JAFFE [10] and CK [11] datasets. Consequently, numerous subsequent research works have employed subregion-based feature extraction and fusion schemes for OFER [12; 13], which initially detect occluded regions and then remove them. Sub-region analysis methods [14; 15] delineate regions based on the analysis of facial feature points, employing an attention mechanism to focus on crucial regions. However, the absence of discernible facial feature points due to occlusion may result in errors in the recognition process. The unoccluded image network-assisted method [16; 17] employs two distinct networks; one is trained on unoccluded images and the other on occluded images. This method employs unoccluded images as privileged information to facilitate expression recognition in occluded scenarios. Nevertheless, this approach is not applicable in real-world scenarios, as it is challenging to ensure the consistent availability of unoccluded images in practical applications. Moreover, there are a number of techniques that are specifically designed for the detection, removal, and reconstruction of occluded regions [18]. It has been argued that the explicit removal of occluded regions is impractical, given the difficulty of precisely detecting and reconstructing occlusion. Ref. [15] points out that humans are capable of understanding facial expressions when the face is occluded and can effectively utilize both the regions and the whole face to perceive the semantics conveyed by an incomplete face. In light of these considerations, this paper presents MAFE, a novel approach to addressing the OFER problem by utilizing global, fine-grained, and crucial regional features in the samples. MAFE primarily comprises three modules: multi-feature extraction, region detail feature fusion, and consistent feature recognition. The multi-feature extraction module is responsible for the extraction of both the global features and  
Figure 1: Illustrative examples of occluded samples. The first row depicts some FER samples with faces occluded, while the second row displays the predicted outcomes employing the baseline Pyramid Transformer ResNet-50 (PTIR-50).