Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide the network to create the embedded feature vectors to be highly correlated if they belong to a similar class, and less correlated if they belong to different classes. In addition, the Mean Discriminator component leads the network to make the mean embedded feature vectors of different classes to be less similar to each other. We use Xception network as the backbone of our model, and contrary to previous work, we propose an embedding feature space that contains k feature vectors. Then, the Embedding Discriminator component penalizes the network to generate the embedded feature vectors, which are dissimilar. We trained our model using the combination of our proposed loss functions called Ad-Corre Loss jointly with the crossentropy loss. We achieved a very promising recognition accuracy on AffectNet, RAF-DB, and FER-2013. Our extensive experiments and ablation study indicate the power of our method to cope well with challenging FER tasks in the wild. The code is available on Github

Facial Expression Recognition in the Wild Using Convolutional Neural Networks

Facial Expression Recognition (FER) is the task of predicting a specific facial expression given a facial image. FER has demonstrated remarkable progress due to the advancement of deep learning. Generally, a FER system as a prediction model is built using two sub-modules: 1. Facial image representation model that learns a mapping from the input 2D facial image to a compact feature representation in the embedding space, and 2. A classifier module that maps the learned features to the label space comprising seven labels of neutral, happy, sad, surprise, anger, fear, or disgust. Ultimately, the prediction model aims to predict one of the seven aforementioned labels for the given input image. This process is carried out using a supervised learning algorithm where the model minimizes an objective function that measures the error between the prediction and true label by searching for the best mapping function. Our work is inspired by Deep Metric Learning (DML) approaches to learn an efficient embedding space for the classifier module. DML fundamentally aims to achieve maximal separation in the embedding space by creating compact and well-separated clusters with the capability of feature discrimination. However, conventional DML methods ignore the underlying challenges associated with wild FER datasets, where images exhibit large intra-class variation and inter-class similarity. First, we tackle the extreme class imbalance that leads to a separation bias toward facial expression classes populated with more data (e.g., happy and neutral) against minority classes (e.g., disgust and fear). To eliminate this bias, we propose a discriminant objective function to optimize the embedding space to enforce inter-class separation of features for both majority and minority classes. Second, we design an adaptive mechanism to selectively discriminate features in the embedding space to promote generalization to yield a prediction model that classifies unseen images more accurately. We are inspired by the human visual attention model described as the perception of the most salient visual cues in the observed scene. Accordingly, our attentive mechanism adaptively selects important features to discriminate in the DML\u27s objective function. We conduct experiments on two popular large-scale wild FER datasets (RAF-DB and AffectNet) to show the enhanced discriminative power of our proposed methods compared with several state-of-the-art FER methods