Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition

Facial images with low quality, subjective annotation, severe occlusion, and rare subject identity can lead to the existence of outlier samples in facial expression datasets. These outlier samples are usually far from the center of the dataset in the feature space, resulting in huge differences in feature distribution, which severely restricts the performance of cross-dataset facial expression recognition (FER). To eliminate the influence of outlier samples on cross-dataset FER, we propose an unsupervised domain adaptation (UDA) method called Sample Self-Revised Network (SSRN), which 1) dynamically detects the outlier level of each sample in the source domain to reduce the disturbance of outlier samples to the model training, as well as 2) adaptively revises outlier samples in the source domain to improve transferability of the learned features. Experimental results show that our SSRN outperforms both classic deep UDA methods and state-of-the-art cross-dataset FER results.