Facial expression recognition (FER) has recently attracted attention in computer vision. However, existing methods mostly focus on the explicit performance and overlook their computational resources and memory consumption. Hence, achieving promising performance while maintaining the efficiency of models is still a huge challenge. In this work, we propose a highly efficient Channel-Shift Gabor-ResNet (CS-GResNet) to capture the crucial visual properties in facial images. Concretely, we incorporate the Gabor Convolution (GConv) into ResNet to produce the significant GResNet as our backbone with limited memory cost. Furthermore, we adopt an extremely simple yet effective Channel-Shift Module inserted into the GResNet to obtain the facial informative representation via facilitating information exchanged among neighboring channels. We conduct extensive experiments on three wild datasets: RAF-DB, FER2013 and SFEW. The results show that our proposed CS-GResNet achieves superior performance against the state-of-the-art methods with less computational and memory cost. Codes are available at https://github.com/jsesr/CS-GResNet-PyTorch.

CS-GResNet: A Simple and Highly Efficient Network for Facial Expression Recognition

Effective attention feature reconstruction loss for facial expression recognition in the wild

Facial expression methods play a vital role in human–computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different datasets, that result in large variations in recognition rates and low accuracy rates for different categories of facial expression datasets. This study introduces RCL-Net, a method of recognizing wild facial expressions that is based on an attention mechanism and LBP feature fusion. The structure consists of two main branches, namely the ResNet-CBAM residual attention branch and the local binary feature (LBP) extraction branch (RCL-Net). First, by merging the residual network and hybrid attention mechanism, the residual attention network is presented to emphasize the local detail feature information of facial expressions; the significant characteristics of facial expressions are retrieved from both channel and spatial dimensions to build the residual attention classification model. Second, we present a locally improved residual network attention model. LBP features are introduced into the facial expression feature extraction stage in order to extract texture information on expression photographs in order to emphasize facial feature information and enhance the recognition accuracy of the model. Lastly, experimental validation is performed using the FER2013, FERPLUS, CK+, and RAF-DB datasets, and the experimental results demonstrate that the proposed method has superior generalization capability and robustness in the laboratory-controlled environment and field environment compared to the most recent experimental methods.

https://www.mdpi.com/1424-8220/23/9/4204/pdf?version=1682166362

Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP

MPCSAN: multi-head parallel channel-spatial attention network for facial expression recognition in the wild

The most discriminative expression features are mostly concentrated in local key facial regions. Thus, we propose a simple and efficient framework that can learn more discriminative expression features from scrambled facial images. Specifically, we first divide the input image into local subregions of the same size and shuffle them randomly at a certain range to obtain the damaged image to increase the difficulty of recognition. Then, the original image and the damaged image are fed to the network. A channel attention module is exploited for highlighting the effective features and suppressing irrelevant features. Simultaneously, during the reconstruction phase, a region alignment model is appended to establish the semantic correlation between each subregion, aiming at restoring the original spatial layout of local subregions in the original image. Extensive experiments on the RAF-DB and the FERPlus datasets demonstrate that our proposed method significantly outperformed state-of-the-art methods without any external facial expression pretraining.

Shuxiang Song

Papers

Destruction and Reconstruction Learning for Facial Expression Recognition