Fine-Grained Facial Expression Recognition in Multiple Smiles

Abstract

Smiling has often been incorrectly interpreted as “happy” in the popular facial expression datasets (AffectNet, RAF-DB, FERPlus). Smiling is the most complex human expression, with positive, neutral, and negative smiles. We focused on fine-grained facial expression recognition (FER) and built a new smiling face dataset, named Facial Expression Emotions. This dataset categorizes smiles into six classes of smiles, containing a total of 11,000 images labeled with corresponding fine-grained facial expression classes. We propose Smile Transformer, a network architecture for FER based on the Swin Transformer, to enhance the local perception capability of the model and improve the accuracy of fine-grained face recognition. Moreover, a convolutional block attention module (CBAM) was designed, to focus on important features of the face image and suppress unnecessary regional responses. For better classification results, an image quality evaluation module was used to assign different labels to images with different qualities. Additionally, a dynamic weight loss function was designed, to assign different learning strategies according to the labels during training, focusing on hard yet recognizable samples and discarding unidentifiable samples, to achieve better recognition. Overall, we focused on (a) creating a novel dataset of smiling facial images from online annotated images, and (b) developing a method for improved FER in smiling images. Facial Expression Emotions achieved an accuracy of 88.56% and could serve as a new benchmark dataset for future research on fine-grained FER.