Important progresses have been made in the field of artificial intelligence in recent years, and facial expression recognition (FER), which could greatly facilitate the development of human-computer interaction, has been becoming a significant research hotspot. In this paper, a novel nonnegative tensor factorization method is proposed based on low-rank subspace (NTF₋LRS) for FER. Firstly, in order to find the high order correlations underlying multi-dimensional data, a data tensor model is constructed, which could represent different dimensional features ingeniously. And then, the low-rank subspace model is adopted to reconstruct the original tensor model, reduce the redundancy of the learned new tensor, and improve the discriminant abilities of inter-class information. Finally, the reconstructed tensor is decomposed to get factor matrices by nonnegative tensor factorization, where all factor matrices are used to extract subspace features. To verify the effectiveness of our proposal, two well-known facial expression datasets named as “JAFFE” and “CK+” are utilized for evaluation, and the experimental results show that the tensor-based method preserves the original structure of whole samples, which avoids the case of dimension curse because of vectorization. In addition, this method uses Laplacian graph to impose regularization on low-rank subspace model, which keeps the local relationship between sample neighbors.

近年来，在人工智能领域已经取得了重要的进展，可以极大地促进人机交互发展的面部表情识别（FER）已经成为一个重要的研究热点。在本文中，一种新颖的非负张量分解方法是基于低秩子空间（NTF提出_-LRS）for FER。首先，为了找到多维数据下的高阶相关性，构建了一个数据张量模型，该模型可以巧妙地表示不同的维数特征。然后，采用低秩子空间模型重构原始张量模型，减少学习到的新张量的冗余度，提高类间信息的判别能力。最后，重构的张量通过非负张量分解分解为因子矩阵，其中所有因子矩阵均用于提取子空间特征。为了验证我们的建议的有效性，使用了两个著名的面部表情数据集“ JAFFE”和“ CK +”进行评估，实验结果表明，基于张量的方法保留了整个样本的原始结构，避免了由于向量化而引起的尺寸诅咒。另外，该方法使用拉普拉斯图对低秩子空间模型进行正则化，从而保持样本邻居之间的局部关系。