The accuracy of facial expression recognition is typically affected by the following factors: high similarities across different expressions, disturbing factors, and micro-facial movement of rapid and subtle changes. One potentially viable solution for addressing these barriers is to exploit the neutral information concealed in neutral expression images. To this end, in this paper we propose a self-Paced Neutral Expression-Disentangled Learning (SPNDL) model. SPNDL disentangles neutral information from facial expressions, making it easier to extract key and deviation features. Specifically, it allows to capture discriminative information among similar expressions and perceive micro-facial movements. In order to better learn these neutral expression-disentangled features (NDFs) and to alleviate the non-convex optimization problem, a self-paced learning (SPL) strategy based on NDFs is proposed in the training stage. SPL learns samples from easy to complex by increasing the number of samples selected into the training process, which enables to effectively suppress the negative impacts introduced by low-quality samples and inconsistently distributed NDFs. Experiments on three popular databases (i.e., CK+, Oulu-CASIA, and RAF-DB) show the effectiveness of our proposed method.

翻译：面部表情识别的准确性通常受以下因素影响：不同表情间的高度相似性、干扰因素以及快速而细微的微面部运动。解决这些障碍的一个可行方案是利用隐藏在中性表情图像中的中性信息。为此，本文提出了一种自步态中性表情解耦学习（SPNDL）模型。SPNDL将中性信息从面部表情中解耦出来，从而更容易提取关键特征和偏差特征。具体而言，它能够捕获相似表情间的判别信息，并感知微面部运动。为了更好学习这些中性表情解耦特征（NDFs）并缓解非凸优化问题，在训练阶段提出了一种基于NDFs的自步学习（SPL）策略。SPL通过逐步增加参与训练过程的样本数量，实现从简单到复杂的样本学习，从而有效抑制低质量样本和NDFs分布不一致带来的负面影响。在三个常用数据库（即CK+、Oulu-CASIA和RAF-DB）上的实验证明了所提方法的有效性。