Dual-Domain Update and Double-Group Optimization Network For Image Compressive Sensing

Facial Expression Recognition (FER) in the wild is a significant yet challenging classification task due to the inter-class similarities and intra-class variations. Recently, a large number of methods can extract expression features effectively. However, the intra-class variations mainly caused by various uncertainties (such as identity, pose, and occlusion) are difficult to capture in advance, and the cost of labeling these uncertainties is high. To tackle this challenge, we propose a novel Mirrored Self-supervised Learning FER (MSL-FER) method. The ground truth of self-supervised learning comes from the data itself rather than from human annotations, and horizontal inversion preserves emotional information without altering the facial structure. Specifically, MSL-FER introduces a binary classification task to recognize the 2D mirror operation in a self-supervised learning method. and we also combine our MSL-FER with an attention network to discriminate features along its dimensions selectively. Experiments on two public wild FER datasets show that our MSL-FER approach outperforms the baseline and other state-of-the-art methods with 87.92% on RAF-DB and 70.68% on FER2013.