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度通常受到以下因素的影响:不同表情之间的相似度高,干扰因素和微小面部运动的快速和微小变化。解决这些障碍的一个可能可行的解决方案是利用隐藏在中性表情图像中的中性信息。为此,本文提出了一种自适应中和表情解缠学习(SPNDL)模型。SPNDL从面部表情中分离出中性信息,容易提取关键信息和偏差特征。具体而言,它允许捕捉类似表情中的有区别的信息,感知微小面部运动。为了更好地学习这些中和表情解缠特征(NDF),并缓解非凸优化问题,训练阶段提出了一种基于NDF的自适应学习(SPL)策略。通过逐步增加选择进入训练过程的样本数量,SPL从简单到复杂地学习样本,从而有效地抑制低质量样本和不一致分布的NDF引入的负面影响。在三个流行的数据库(即CK +,Oulu-CASIA和RAF-DB)上的实验表明了我们提出的方法的有效性。