[1](America) 施塔, (America) 卡拉特. 情绪心理学[M]. 中国轻工业出版社. 2015.[2]Ruan D., Yan Y., Lai S., et al. Feature decomposition and reconstruction learning for effective facial expression recognition[A]. Proceedings of 45th the IEEE/CVF Conference on Computer Vision and Pattern Recognition[C]. Greek: IEEE, 2021: 7660-7669.[3]Xue F., Wang Q., Guo G.. Transfer: Learning relation-aware facial expression representations with transformers[A]. Proceedings of 45th the IEEE/CVF International Conference on Computer Vision[C]. IEEE, 2021: 3601-3610.[4]Wang C., Wang S., Liang G.. Identity-and pose-robust facial expression recognition through adversarial feature learning[A]. Proceedings of the 27th ACM international conference on multimedia[C]. Nice, France: ACM, 2019: 238-246.[5]Wang K., Peng X., Yang J., et al. Region attention networks for pose and occlusion robust facial expression recognition[J]. IEEE Transactions on Image Processing, 2020, V29: 4057-4069.[6]Li Y., Zeng J., Shan S., et al. Occlusion aware facial expression recognition using CNN with attention mechanism[J]. IEEE Transactions on Image Processing, 2018, V28(5): 2439-2450.[7]Wu Q., Dai P., Chen J., et al. Discover cross-modality nuances for visible-infrared person re-identification[A]. Proceedings of 45th the IEEE/CVF Conference on Computer Vision and Pattern Recognition[C]. IEEE, 2021: 4330-4339.[8]Dosovitskiy A., Beyer L., Kolesnikov A., et al. An image is worth 16x16 words: Transformers for image recognition at scale[EB/OL]. arXiv:2010.11929, 2020.[9]He K., Zhang X., Ren S., et al. Deep residual learning for image recognition[A], Proceedings of 40th the IEEE/CVF Conference on Computer Vision and Pattern Recognition[C]. Las Vegas: IEEE, 2016: 770-778.[10]Zhang Y., Wang C., Deng W.. Relative uncertainty learning for facial expression recognition[J]. Advances in Neural Information Processing Systems, 2021, V34: 17616- 17627.