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

Facial expression recognition (FER) in the wild is very challenging due to occlusion, posture, illumination, and other uncontrolled factors. Learning discriminant features for FER using Convolutional Neural Networks is a momentous task for the significant class imbalance, wrong labels, inter-class similarities, and intra-class variations. The traditional method utilizes the Cross entropy loss function to optimize the convolutional network to obtain discriminative features for classification. However, this loss function cannot effectively solve the above problems in practice and cannot contribute to obtaining highly discriminant facial features for further analysis. Center loss improves the learning efficiency by reducing the intra-class distance of similar expressions, while the improvement of inter-class similarity, class imbalance, and generalization is insufficient. In this paper, we propose a lightweight Effective Attention Feature Reconstruction loss (EAFR loss), which can further optimize the feature space and enhance the discriminability of expression. The loss model is composed of the Focal Smoothing loss (FS loss) and the Aggregation-Separation loss (AS loss). Firstly, the FS loss can improve the poor recognition performance caused by imbalanced classes and prevent paranoid knowledge learning behaviors. Meanwhile, AS loss further accurately condenses the intra-class expression features and expands the inter-class distance, which is achieved by using progressive stage max-pooling channel and position attention mechanism and lightweight asymmetric autoencoder model for feature reconstruction. Finally, the EAFR loss joins the above two loss functions to more comprehensively solve the above typical problems for FER in the wild. We validate the proposed loss function on three most commonly used large-scale wild expression datasets (RAF-DB, FERPlus, and AffectNet), and the results show that our model achieves superior performance to several state-of-the-art methods.