[1] Tian C Z, Song M, Tian J W et al. Combination weighting and FCE based evaluation for human-computer interaction effectiveness of command and control system[J]. Electron Opt Control, 31, 87-96(2024).

[2] Li Y H, Lü X Q, Gu Y et al. Face detection algorithm based on improved S3FD network[J]. Laser Technol, 45, 722-728(2021).

[3] Sun R, Shan X Q, Sun Q J et al. NIR-VIS face image translation method with dual contrastive learning framework[J]. Opto-Electron Eng, 49, 210317(2022).

[4] Wang K, Peng X J, Yang J F et al. Suppressing uncertainties for large-scale facial expression recognition[C], 6896-6905(2020).

[5] Zhang W X, Luo Y H, Liu Y Q et al. Image super-resolution reconstruction based on active displacement imaging[J]. Opto-Electron Eng, 51, 230290(2024).

[6] Liu C, Cao L C, Jin Y et al. Transformer for age-invariant face recognition[J]. Laser Optoelectron Prog, 60, 1010019(2023).

[7] Yaddaden Y, Adda M, Bouzouane A. Facial expression recognition using locally linear embedding with LBP and HOG descriptors[C], 221-226(2021). https://doi.org/10.1109/IHSH51661.2021.9378702

[8] Wang K, Peng X J, Yang J F et al. Region attention networks for pose and occlusion robust facial expression recognition[J]. IEEE Trans Image Process, 29, 4057-4069(2020).

[9] Wasi A T, Šerbetar K, Islam R et al. ARBEx: attentive feature extraction with reliability balancing for robust facial expression learning[Z](2024). https://doi.org/10.48550/arxiv.2305.01486

[10] Liu Y Z, Xu Z M, Lang C Y et al. Fine-grained facial expression recognition algorithm based on relationship-awareness and label disambiguation[J]. Acta Electron Sin, 52, 3336-3346(2024).

[11] Chen Y, Wu L C, Wang C. A micro-expression recognition method based on multi-level information fusion network[J]. Acta Autom Sin, 50, 1445-1457(2024).

[12] Zhang C C, Wang S, Wang W Y et al. Adversarial background attacks in a limited area for CNN based face recognition[J]. Opto-Electron Eng, 50, 220266(2023).

[13] Wei X G. Research on facial expression recognition method based on convolutional neural network[D](2023). https://doi.org/10.27272/d.cnki.gshdu.2023.006762

[14] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 6000-6010(2017).

[15] Chen M, Radford A, Child R et al. Generative pretraining from pixels[C], 1691-1703(2020).

[16] Devlin J, Chang M W, Lee K et al. BERT: pre-training of deep bidirectional transformers for language understanding[C], 4171-4186(2019). https://doi.org/10.18653/v1/N19-1423

[17] Liu C, Hirota K, Dai Y P. Patch attention convolutional vision transformer for facial expression recognition with occlusion[J]. Inf Sci, 619, 781-794(2023).

[18] Chen X C, Zheng X W, Sun K et al. Self-supervised vision transformer-based few-shot learning for facial expression recognition[J]. Inf Sci, 634, 206-226(2023).

[19] Zheng C, Mendieta M, Chen C. POSTER: a pyramid cross-fusion transformer network for facial expression recognition[C], 3138-3147(2023). https://doi.org/10.1109/ICCVW60793.2023.00339

[20] Liu Z, Lin Y T, Cao Y et al. Swin Transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021). https://doi.org/10.1109/ICCV48922.2021.00986

[21] Feng H Q, Huang W K, Zhang D H et al. Fine-tuning Swin Transformer and multiple weights optimality-seeking for facial expression recognition[J]. IEEE Access, 11, 9995-10003(2023).

[22] Pinasthika K, Laksono B S P, Irsal R B P et al. SparseSwin: Swin Transformer with sparse transformer block[J]. Neurocomputing, 580, 127433(2024).

[23] Ouyang D L, He S, Zhang G Z et al. Efficient multi-scale attention module with cross-spatial learning[C], 1-5(2023). https://doi.org/10.1109/ICASSP49357.2023.10096516

[24] Khaled A, Li C, Ning J et al. BCN: batch channel normalization for image classification[Z](2023). https://doi.org/10.48550/arxiv.2312.00596

[25] Bodavarapu P N R, Srinivas P V V S. Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques[J]. Indian J Sci Technol, 14, 971-983(2021).

[26] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018). https://doi.org/10.1109/CVPR.2018.00474

[27] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2019). https://doi.org/10.1109/ICCV.2019.00140

[28] Fard A P, Mahoor M H. Ad-corre: adaptive correlation-based loss for facial expression recognition in the wild[J]. IEEE Access, 10, 26756-26768(2022).

[29] Zhu Y C, Wei L L, Lang C Y et al. Fine-grained facial expression recognition via relational reasoning and hierarchical relation optimization[J]. Pattern Recognit Lett, 164, 67-73(2022).

[30] Li H Y, Wang N N, Yang X et al. Towards semi-supervised deep facial expression recognition with an adaptive confidence margin[C], 4156-4165(2022). https://doi.org/10.1109/CVPR52688.2022.00413