Deep reinforcement learning for robust emotional classification in facial expression recognition

Abstract For emotion classification in facial expression recognition (FER), the performance of both traditional statistical methods and state-of-the-art deep learning methods are highly dependent on the quality of data. Traditional methods use image preprocessing (such as smoothing and segmentation) to improve image quality. However, the results still fail to meet the quality requirements of the emotion classifiers in FER. To address the above issues, this paper proposed a novel framework based on reinforcement learning for pre-selecting useful images(RLPS) for emotion classification in FER, which is made up of two modules: image selector and rough emotion classifier. Image selector is used to select useful images for emotion classification through reinforcement strategy and rough emotion classifier acts as a teacher to train image selector. Our framework improves classification performance by improving the quality of the dataset and can be applied to any classifier. Experiment results on RAF-DB, ExpW, and FER2013 datasets show that the proposed strategy achieves consistent improvements compared with the state-of-the-art emotion classification methods in FER. 1