Facial Expression Recognition Using Local Sliding Window Attention

Abstract

There are problems associated with facial expression recognition (FER), such as facial occlusion and head pose variations. These two problems lead to incomplete facial information in images, making feature extraction extremely difficult. Most current methods use prior knowledge or fixed-size patches to perform local cropping, thereby enhancing the ability to acquire fine-grained features. However, the former requires extra data processing work and is prone to errors; the latter destroys the integrity of local features. In this paper, we propose a local Sliding Window Attention Network (SWA-Net) for FER. Specifically, we propose a sliding window strategy for feature-level cropping, which preserves the integrity of local features and does not require complex preprocessing. Moreover, the local feature enhancement module mines fine-grained features with intraclass semantics through a multiscale depth network. The adaptive local feature selection module is introduced to prompt the model to find more essential local features. Extensive experiments demonstrate that our SWA-Net model achieves a comparable performance to that of state-of-the-art methods with scores of 90.03% on RAF-DB, 89.22% on FERPlus, 63.97% on AffectNet.

Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

“…We employ a sliding window cropping strategy [34] in contrast to the majority of expression recognition techniques, which use fixed cropping regions to get adequately rich local regions of the face. The sliding window cropping process uses the global feature map, which was produced from the feature extraction layer, as the target, and adjusts the number of subregions based on the size of the cropped subregions and as hyperparameters to obtain various numbers of subregions for the face.…”

Hybrid Domain Consistency Constraints-Based Deep Neural Network for Facial Expression Recognition

“…Their proposed local feature enhancement module mines fine-grained features with intra-class semantics through a multiscale, deep network. They introduced an adaptive local feature selection module to guide the model to find more of the essential local features [10].…”