[1]	吴奇,申寻兵,傅小兰.微表情研究及其应用[J].心理科学进展,2010,18(9):1359-1368. Cited in this article [1]

[2]	Ekman P, Friesen W V. Detecting deception from the body or face[J]. Journal of personality and social psychology, 1974, 29(3):288-298. Cited in this article [1]

[3]

HOUSE C, MEYER R. Preprocessing and descriptor features for facial micro-expression recognition [EB/OL].[2022-07-30].https://web.stanford.edu/classlee368/Project_Spring_1415/Reports/House_Meyer.pdf. https://web.stanford.edu/classlee368/Project_Spring_1415/Reports/House_Meyer.pdf Cited in this article [1]

[4]	陈子健,朱晓亮.基于面部表情的学习者情绪自动识别研究——适切性、现状、现存问题和提升路径[J].远程教育杂志,2019,37(4):64-72. Cited in this article [1]

[5]	Zeng Z h, Pantic M, Roisman G I, et al. A survey of affect recognition methods: audio, visual, and spontaneous expression[J].IEEE trans on pattern analysis and machine intelligence,2009,31(1):39-58. Cited in this article [1]

[6]	ZHANG J, DONALD A N. Representations in distributed cognitive tasks[J]. Cognitive science,1994,18(1):87-122. Cited in this article [1]

[7]	阳长征.危机事件中网络信息表征对用户持续分享意愿影响研究[J].图书情报工作,2019,63(21):105-116. Cited in this article [1]

[8]	石程旭.监狱民警应用微表情分析的思考[J].法制博览,2022(5):27-29. Cited in this article [1]

[9]	崔小洛.基于微表情追踪的课堂教学效果即时反馈系统设计[J].无线互联科技,2022,19(4):52-54. Cited in this article [1]

[10]	李婧婷,东子朝,刘烨,等.基于人类注意机制的微表情检测方法[J].心理科学进展,2022,30(10):2143-2153. Cited in this article [1]

[11]	Frank M G, Herbasz M, Sinuk K, et al. I see how you feel: training laypeople and professionals to recognize fleeting emotion[C]// The annual meeting of the International Communication Association. New York: International Communication Association, 2009: 1-35. Cited in this article [1]

[12]	Pfister T, Li X, Zhao G, et al. Recognising spontaneous facial micro-expressions[C]//2011 international conference on computer vision. Piscataway: IEEE, 2011: 1449-1456. Cited in this article [1]

[13]	徐峰,张军平.人脸微表情识别综述[J].自动化学报,2017,43(3):333-348. Cited in this article [1]

[14]	Pfister T, Li X B, Zhao G Y, et al. Recognising spontaneous facial micro-expressions[C]//Proceedings of the 2011 IEEE international conference on computer vision. Piscataway: IEEE,2011:1449-1456. Cited in this article [1]

[15]	Li X B, Pfister T, Huang X H, et al. Aspontaneous micro-expression database: inducement, collection and baseline[C]//Proceedings of the 10th IEEE international conference and workshops on automatic face and gesture recognition. Piscataway: IEEE,2013:1-6. Cited in this article [1]

[16]	Yan w J, Wu Q, Liu Y J, et al. CASME database: a dataset of spontaneous micro-expressions collected from neutralized faces[C]//Proceedings of the 10th IEEE international conference and workshops on automatic face and gesture recognition. Piscataway: IEEE, 2013:1-7. Cited in this article [1]

[17]	Yan W J, Li X, Wang S J, et al. CASME II: an improved spontaneous micro-expression database and the baseline evaluation[J]. PloS one, 2014, 9(1): e86041. Cited in this article [1]

[18]	Qu F, Wang S J, Yan W J, et al. CAS (ME)^2: a database for spontaneous macro-expression and micro-expression spotting and recognition[J]. IEEE transactions on affective computing, 2017, 9(4): 424-436. Cited in this article [1]

[19]	Li J, Dong Z, Lu S, et al. CAS (ME)3: a third generation facial spontaneous micro-expression database with depth information and high ecological validity[J]. IEEE transactions on pattern analysis and machine intelligence, 2022,45(3): 2782-2800. Cited in this article [1]

[20]	Yap C H, Kendrick C, Yap M H. SAMM long videos: a spontaneous facial micro-and macro-expressions dataset[C]//2020 15th IEEE international conference on automatic face and gesture recognition. Piscataway: IEEE, 2020: 771-776. Cited in this article [1]

[21]	Ben X,Ren Y, Zhang J, et al. Video-based facial micro-expression analysis: a survey of datasets, features and algorithms[J]. IEEE transactions on pattern analysis and machine intelligence, 2021,44(9): 5826-5846. Cited in this article [1]

[22]	Pfister T, Li X, Zhao G, et al. Recognising spontaneous facial micro-expressions[C]//2011 international conference on computer vision. Piscataway: IEEE, 2011: 1449-1456. Cited in this article [1]

[23]	Polikovsky S, Kameda Y, Ohta Y. Facial micro-expression detection in hi-speed video based on facial action coding system (FACS)[J]. IEICE transactions on information and systems, 2013, 96(1): 81-92. Cited in this article [1]

[24]	Rowley H A, Baluja S, Kanade T. Neural network-based face detection[J] IEEE transactions on pattern analysis and machine intelligence, 1998, 20(1): 23-38. Cited in this article [1]

[25]	Schapire E, Singer Y. Improved boosting algorithms using confidence-rated predictions[J].Machine learning,1999,37(3):297-336. Cited in this article [1]

[26]	COOTES T F, TAYLOR C J, EDWARDS G J. Active appearance models[C]//European conference on computer vision. Berlin: Springer,1998:484-498. Cited in this article [1]

[27]	Wadhwa N, Rubinstein M, Durand F, et al. Phase-based video motion processing[J]. ACM transactions on graphics, 2013, 32(4):1-10. Cited in this article [1]

[28]	姚海燕,李健,邓小昭.网络用户信息行为研究中的隐私问题探讨[J].情报探索,2010(7):14-16. Cited in this article [1]

[29]	周霞,王萍,王美月,等.政府开放数据用户认知影响因素研究——先验图式调节效应[J].情报科学,2022,40(9):159-166. Cited in this article [1]

[30]	蒋福明,曾慧平.人脸识别技术应用中的隐私伦理问题及其消解路径[J].山西高等学校社会科学学报,2020,32(9):19-24. Cited in this article [1]

[31]	李思宁. 基于深度学习的面部微表情识别方法研究[D]. 徐州：中国矿业大学, 2020. Cited in this article [1]

[32]	Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE transactions on pattern analysis machine intelligence, 2002,24(7): 971-987.

[33]	Zhao G, Pietikainen M. Dynamic texture recognition using local binary patterns with an application to facial expressions[J]. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(6): 915-928.

[34]	Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//IEEE computer society conference on computer vision & pattern recognition. Piscataway: IEEE, 2005: 886-893.

[35]	Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision[C]//Proceedings of the 7th international joint conference on artificial intelligence. San Francisco: Morgan Kaufmann Publishers, 1997, 2: 674-679.

[36]	Chaudhry R, Ravichandran A, Hager G, et al. Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions[C]//Proceedings of 2009 IEEE conference on computer vision and pattern recognition. Miami: IEEE, 2009: 1932-1939.

[37]	Liu Y J, Zhang J K, Yan W J, et al. A main directional mean optical flow feature for spontaneous micro-expression recognition[J]. IEEE transactions on affective computing, 2015, 7(4): 299-310.

[38]	Lu Z, Luo Z, Zheng H, et al. A delaunay-based temporal coding model for micro-expression recognition [C]//Asian conference on computer vision. Singapore: Springer, 2014: 698-711.

[39]	Takalkar M A, Xu M. Image based facial micro-expression recognition using deep learning on small datasets[C]//2017 international conference on digital image computing: techniques and applications. Piscataway: IEEE, 2017: 1-7. Cited in this article [1]

[40]	钱付兰,李建红,赵姝,等.基于深度混合模型评分推荐[J].南京航空航天大学学报,2019,51(5):592-598. Cited in this article [1]

[41]	刘德志, 梁正友, 孙宇. 结合空间注意力机制与光流特征的微表情识别方法 [J]. 计算机辅助设计与图形学学报, 2021,33(10):1541-1552.

[42]	Khor H Q, See J, Phan R, et al. Enriched long-term recurrent convolutional network for facial micro-expression recognition [C]// IEEE international conference on automatic face & gesture recognition. Piscataway: IEEE, 2018: 667-674.

[43]	YAO L, XIAO X, CAO R, et al. Three stream 3D CNN with SE block for micro-expression recognition[C]//2020 International conference on computer engineering and application. Piscataway: IEEE, 2020:439-443

[44]	李星燃,张立言,姚树婧.结合特征融合和注意力机制的微表情识别方法[J].计算机科学,2022,49(2):4-11.

[45]	LIU Y Y, DAI W, FANG F, et al. Dynamic multi- channel metric network for joint pose-aware and identity-invariant facial expression recognition[J]. Information sciences,2021(578):195-213.

[46]	孔慧芳,钱世超,闫嘉鹏.基于不均衡数据与迁移学习的面部微表情识别[J].合肥工业大学学报(自然科学版),2020,43(7):895-900.

[47]	刘洋,马莉莉,张雯,等.基于跨模态深度学习的旅游评论反讽识别[J].数据分析与知识发现,2022,6(12):23-31. Cited in this article [1]

[48]	阳长征.危机事件中网络信息表征对用户持续分享意愿影响研究[J].图书情报工作,2019,63(21):105-116. Cited in this article [1]

[49]	韩丽,李洋,周子佳,等.课堂环境中基于面部表情的教学效果分析[J].现代远程教育研究,2017(4):97-103,112. Cited in this article [1]

[50]	夏乾馨,付强.应用微表情识别技术实现公安预警模式的探讨[J].中国防伪报道,2021(2):82-85. Cited in this article [1]

[51]	康桐瑞.论微表情分析在我国侦查讯问中的应用[J].上海公安学院学报,2019,29(4):28-33. Cited in this article [1]

[52]	Kang J, CHEN X Y, LIU Q Y, et al. Research on a micro-expression recognition technology based on multimodal fusion[J/OL]. Complexity, 2021[2023-03-02]. https://www.hindawi.com/journals/complexity/2021/5221950/. https://www.hindawi.com/journals/complexity/2021/5221950/ Cited in this article [1]

[53]	Buhari A M, Ooi C P, Baskaran V M, et al. Invisible emotion magnification algorithm (IEMA) for real-time micro-expression recognition with graph-based features[J]. Multimedia tools and applications, 2022, 81(7): 9151-9176. Cited in this article [1]

[54]	Liu J, Wang H, Feng Y. An end-to-end deep model with discriminative facial features for facial expression recognition[J]. IEEE access, 2021, 9: 12158-12166. Cited in this article [1]

[55]	El Zarif N, Montazeri L, Leduc-Primeau F, et al. Mobile-optimized facial expression recognition techniques[J]. IEEE access, 2021, 9: 101172-101185. Cited in this article [1]

[56]	Zhang G, Lv G, Binsawad M, et al. Dynamic nonlinear expression recognition technology using neural network and attention mechanism[J]. Fractals, 2022, 30(2): 2240097. Cited in this article [1]

[57]	陈子健,朱晓亮.基于面部表情的学习者情绪自动识别研究——适切性、现状、现存问题和提升路径[J].远程教育杂志,2019,37(4):64-72. Cited in this article [1]

[58]	彭小江,乔宇.面部表情分析进展和挑战[J].中国图象图形学报,2020,25(11):2337-2348. Cited in this article [1]

[59]	马艳准. 多民族面部表情理解分析技术研究[D].沈阳: 东北大学,2012. Cited in this article [1]

[60]	张旭东,刘洋.组态视角下元宇宙图书馆用户接受意愿影响因素研究[J].图书馆理论与实践,2023(3):73-85. Cited in this article [1]

[61]	于明,钟元想,王岩.人脸微表情分析方法综述[J].计算机工程,2023,49(2):1-14. Cited in this article [1]

[62]	殷明,张剑心,史爱芹,等.微表情的特征、识别、训练和影响因素[J].心理科学进展,2016,24(11):1723-1736. Cited in this article [1]

[63]	樊振佳,宋正刚,刘鸿彬,等.贫困地区返乡创业人员信息获取不平等表征及其根源分析[J].情报科学,2019,37(10):81-86,113. Cited in this article [1]

[64]	刘汝涵,徐丹.视频放大和深度学习在微表情识别任务上的应用[J].计算机辅助设计与图形学学报,2019,31(9):1535-1541. Cited in this article [1]

[65]	姜婷婷,吴茜,徐亚苹,等.眼动追踪技术在国外信息行为研究中的应用[J].情报学报,2020,39(2):217-230. Cited in this article [1]

[66]	吴冉,任衍具.微表情的启动效应研究[J].应用心理学,2011,17(3):241-248. Cited in this article [1]

[67]	刘缘,庾永波.在安检中加强“微表情”识别的思考——基于入藏公路安检的考察[J].四川警察学院学报,2019,31(1):61-68. Cited in this article [1]

[68]	Shen X, Wu Q, Fu X. Effects of the duration of expressions on the recognition of micro expressions[J]. Journal of Zhejiang University science B, 2012, 13(3): 221-230. Cited in this article [1]

[69]	刘洋,马莉莉,张雯,等.基于跨模态深度学习的旅游评论反讽识别[J].数据分析与知识发现,2022,6(12):23-31. Cited in this article [1]

[70]	谭春辉,陈晓琪,梁远亮,等.隐私泄露事件中社交媒体围观者情感分析[J].情报科学,2023,41(3):8-18. Cited in this article [1]