Conventional feature extraction methods generally focus on extracting global and local features from the original data or converting a high dimensional space to a lower dimensional one. However, they tend to overlook the discriminative information of pixel values hidden in the original data. Pixel values in some local parts of a face, such as the mouth, eyebrows and eyes, provide extremely useful information for expression recognition, as they reveal the correlation between these local parts. While this information can be learned manually, being able to automatically identify important location information in this context is highly desirable. Given this, we propose a self-adaptive feature learning approach based on a priori knowledge for facial expression recognition in this paper. The proposed approach aims to adaptively select active features. It first generates an intra-class, low-rank dictionary that can decouple the original space from the expression subspace and mitigate the dependence on individual facial identities. Next, the active feature dictionary is formed, taking both global and local importance into account simultaneously. After that, the principal component of the active feature dictionary is extracted to address the influence of redundant features and reduce the dimension. We also introduce an active feature learning model as the final classification framework to make the features more discriminative and reduce the computation time. Results of comprehensive experiments on public facial expression datasets confirm the efficacy of the proposed approach, in terms of accuracy and computation time, compared to some state-of-the-art feature extraction and dictionary learning methods.

常规特征提取方法通常集中于从原始数据中提取全局和局部特征，或将高维空间转换为低维空间。但是，它们倾向于忽略隐藏在原始数据中的像素值的判别信息。面部某些局部部分（例如嘴，眉毛和眼睛）的像素值为表情识别提供了非常有用的信息，因为它们揭示了这些局部部分之间的相关性。尽管可以手动学习此信息，但是在这种情况下，能够自动识别重要的位置信息是非常需要的。鉴于此，本文提出了一种基于先验知识的面部表情识别自适应特征学习方法。所提出的方法旨在自适应地选择活动特征。它首先生成一个类内的，低级的字典，该字典可以将原始空间与表达式子空间解耦，并减轻对各个面部身份的依赖性。接下来，形成活动特征字典，同时考虑全局和局部重要性。之后，提取活动特征字典的主要成分以解决冗余特征的影响并减小尺寸。我们还将引入主动特征学习模型作为最终的分类框架，以使特征更具区分性并减少计算时间。在公开的面部表情数据集上进行的综合实验结果证实了该方法的有效性，包括准确性和计算时间，