Decoding Group Emotional Dynamics in a Web-Based Collaborative Environment: A Novel Framework Utilizing Multi-Person Facial Expression Recognition

References

• Andiani, F. M., & Soewito, B. (2021). Face recognition for work attendance using multitask convolutional neural network (MTCNN) and pre-trained facenet. ICIC Express Letters, 15(1), 57–65. https://doi.org/10.24507/icicel.15.01.57
   Google Scholar
• Andrean, M. N., Shidik, G. F., Naufal, M., Al Zami, F., Winarno, S., Al Azies, H., & Putra, P. L. W. E. (2024). Comparing Haar cascade and YOLOFACE for region of interest classification in drowsiness detection. Jurnal Media Informatika Budidarma, 8(1), 272–281.
   Google Scholar
• Bakariya, B., Singh, A., Singh, H., Raju, P., Rajpoot, R., & Mohbey, K. K. (2023). Facial emotion recognition and music recommendation system using CNN-based deep learning techniques. Evolving Systems, 15(2), 641–658. https://doi.org/10.1007/s12530-023-09506-z
   Google Scholar
• Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930
   PubMed Web of Science ®Google Scholar
• Barsoum, E., Zhang, C., Ferrer, C. C., & Zhang, Z. (2016, October). Training deep networks for facial expression recognition with crowd-sourced label distribution. In Proceedings of the 18th ACM International Conference on Multimodal Interaction (pp. 279–283). https://doi.org/10.1145/2993148.2993165
   Google Scholar
• Bartkiene, E., Steibliene, V., Adomaitiene, V., Juodeikiene, G., Cernauskas, D., Lele, V., Klupsaite, D., Zadeike, D., Jarutiene, L., & Guiné, R. P. F. (2019). Factors affecting consumer food preferences: Food taste and depression-based evoked emotional expressions with the use of face reading technology. BioMed Research International, 2019, 2097415. https://doi.org/10.1155/2019/2097415
   PubMed Web of Science ®Google Scholar
• Belli, S. (2018). Managing negative emotions in online collaborative learning: A multimodal approach to solving technical difficulties. Digithum, (22), 35–46. https://doi.org/10.7238/d.v0i22.3140
   Google Scholar
• Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2020). Privacy preserving face recognition utilizing differential privacy. Computers & Security, 97, 101951. https://doi.org/10.1016/j.cose.2020.101951
   Web of Science ®Google Scholar
• Chang, W. J., Schmelzer, M., Kopp, F., Hsu, C. H., Su, J. P., Chen, L. B., & Chen, M. C. (2019, February). A deep learning facial expression recognition based scoring system for restaurants. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 251–254). IEEE. https://doi.org/10.1109/ICAIIC.2019.8668998
   Google Scholar
• Chang, W., Jianhua, H., & Xiaoning, S. (2021). Research on the influencing factors of users’ emotion in collaborative information seeking: based on self-efficacy and task complexity. Documentation, Information & Knowledge, (1), 76–84. https://doi.org/10.13366/j.dik.2021.01.076
   Google Scholar
• Chowdary, M. K., Nguyen, T. N., & Hemanth, D. J. (2023). Deep learning-based facial emotion recognition for human–computer interaction applications. Neural Computing and Applications, 35(32), 23311–23328. https://doi.org/10.1007/s00521-021-06012-8
   Web of Science ®Google Scholar
• Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020). Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5203–5212). https://doi.org/10.1109/cvpr42600.2020.00525
   Google Scholar
• Dhavalikar, A. S., & Kulkarni, R. K. (2014, February). Face detection and facial expression recognition system. In 2014 International Conference on Electronics and Communication Systems (ICECS) (pp. 1–7). IEEE. https://doi.org/10.1109/ECS.2014.6892834
   Google Scholar
• Dipeolu, A., Hargrave, S., Leierer, S. J., Tineo, Y. A. C., Longoria, A., & Escalante, M. (2022). Dysfunctional career thoughts and the sophomore slump among students with learning disabilities. Journal of Career Development, 49(4), 862–874. https://doi.org/10.1177/08948453211000130
   Web of Science ®Google Scholar
• Ertay, E., Huang, H., Sarsenbayeva, Z., & Dingler, T. (2021, September). Challenges of emotion detection using facial expressions and emotion visualisation in remote communication. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (pp. 230–236). https://doi.org/10.1145/3460418.3479341
   Google Scholar
• Ezzameli, K., & Mahersia, H. (2023). Emotion recognition from unimodal to multimodal analysis: A review. Information Fusion, 99, 101847. https://doi.org/10.1016/j.inffus.2023.101847
   Web of Science ®Google Scholar
• Fei, Z., Yang, E., Li, D. D. U., Butler, S., Ijomah, W., Li, X., & Zhou, H. (2020). Deep convolution network based emotion analysis towards mental health care. Neurocomputing, 388, 212–227. https://doi.org/10.1016/j.neucom.2020.01.034
   Web of Science ®Google Scholar
• Ge, D. H., Li, H. S., Zhang, L., Liu, R., Shen, P., & Miao, Q. G. (2020). Survey of lightweight neural network. Journal of Software, 31(9), 2627–2653. https://dx.doi.org/10.13328/j.cnki.jos.005942
   Google Scholar
• Ge, H., Zhu, Z., Dai, Y., Wang, B., & Wu, X. (2022). Facial expression recognition based on deep learning. Computer Methods and Programs in Biomedicine, 215, 106621. https://doi.org/10.1016/j.cmpb.2022.106621
   PubMed Web of Science ®Google Scholar
• Gera, D., Balasubramanian, S., & Jami, A. (2022). CERN: Compact facial expression recognition net. Pattern Recognition Letters, 155, 9–18. https://doi.org/10.1016/j.patrec.2022.01.013
   Web of Science ®Google Scholar
• González-Rodríguez, M. R., Díaz-Fernández, M. C., & Gómez, C. P. (2020). Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions. Telematics and Informatics, 51, 101404. https://doi.org/10.1016/j.tele.2020.101404
   Web of Science ®Google Scholar
• Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y. C., Thaler, D., Lee, D. H., Zhou, Y. B., Ramaiah, C., Feng, F. X., Li, R. F., Wang, X. J., Athanasakis, D., Shawe-Taylor, J., Milakov, M., Park, J., … Bengio, Y. (2015). Challenges in representation learning: A report on three machine learning contests. Neural Networks, 64, 59–63. https://doi.org/10.1016/j.neunet.2014.09.005
   Google Scholar
• Grandey, A. A., & Melloy, R. C. (2017). The state of the heart: Emotional labor as emotion regulation reviewed and revised. Journal of Occupational Health Psychology, 22(3), 407–422. https://doi.org/10.1037/ocp0000067
   PubMed Web of Science ®Google Scholar
• Gu, W., Xiang, C., Venkatesh, Y. V., Huang, D., & Lin, H. (2012). Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recognition, 45(1), 80–91. https://doi.org/10.1016/j.patcog.2011.05.006
   Web of Science ®Google Scholar
• Guo, X., Zhou, J., & Xu, T. (2018). Evaluation of teaching effectiveness based on classroom micro-expression recognition. International Journal of Performability Engineering, 14(11), 2877. https://doi.org/10.23940/ijpe.18.11.p33.28772885
   Google Scholar
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). https://doi.org/10.1109/cvpr.2016.90
   Google Scholar
• He, Q., Zhang, H., Mei, Z., & Xu, X. (2023). High accuracy intelligent real-time framework for detecting infant drowning based on deep learning. Expert Systems with Applications, 228, 120204. https://doi.org/10.1016/j.eswa.2023.120204
   Web of Science ®Google Scholar
• Hussain, S. A., & Al Balushi, A. S. A. (2020). A real time face emotion classification and recognition using deep learning model. Journal of Physics: Conference Series, 1432(1), 012087. (IOP Publishing. https://doi.org/10.1088/1742-6596/1432/1/012087
   Google Scholar
• Karnati, M., Seal, A., Bhattacharjee, D., Yazidi, A., & Krejcar, O. (2023). Understanding deep learning techniques for recognition of human emotions using facial expressions: A comprehensive survey. IEEE Transactions on Instrumentation and Measurement, 72, 1–31. https://doi.org/10.1109/TIM.2023.3243661
   PubMed Web of Science ®Google Scholar
• Koval, P., Kalokerinos, E. K., Greenaway, K. H., Medland, H., Kuppens, P., Nezlek, J. B., Hinton, J. D. X., & Gross, J. J. (2023). Emotion regulation in everyday life: Mapping global self-reports to daily processes. Emotion, 23(2), 357–374. https://doi.org/10.1037/emo0001097
   PubMed Web of Science ®Google Scholar
• Krause, F. C., Linardatos, E., Fresco, D. M., & Moore, M. T. (2021). Facial emotion recognition in major depressive disorder: A meta-analytic review. Journal of Affective Disorders, 293, 320–328. https://doi.org/10.1016/j.jad.2021.06.053
   PubMed Web of Science ®Google Scholar
• Kuruvayil, S., & Palaniswamy, S. (2022). Emotion recognition from facial images with simultaneous occlusion, pose and illumination variations using meta-learning. Journal of King Saud University-Computer and Information Sciences, 34(9), 7271–7282. https://doi.org/10.1016/j.jksuci.2021.06.012
   Web of Science ®Google Scholar
• Landowska, A., Brodny, G., & Wrobel, M. R. (2017, April). Limitations of emotion recognition from facial expressions in e-learning context. In International Conference on Computer Supported Education (Vol. 2, pp. 383–389). SciTePress. https://doi.org/10.5220/0006357903830389
   Google Scholar
• Lee, J., Kim, J., Lee, E., & Lee, H. (2023). Deep learning model structure for Korean facial expression detection. The Journal of Korean Institute of Information Technology, 21(2), 9–17. https://doi.org/10.14801/jkiit.2023.21.2.9
   Google Scholar
• Li, D., Mei, H., Shen, Y., Su, S., Zhang, W., Wang, J., Zu, M., & Chen, W. (2018). ECharts: A declarative framework for rapid construction of web-based visualization. Visual Informatics, 2(2), 136–146. https://doi.org/10.1016/j.visinf.2018.04.011
   Google Scholar
• Li, J., Shi, D., Tumnark, P., & Xu, H. (2020). A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure. Peer-to-Peer Networking and Applications, 13(5), 1706–1719. https://doi.org/10.1007/s12083-019-00863-8
   Web of Science ®Google Scholar
• Li, S., Deng, W., & Du, J. (2017). Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2852–2861). https://doi.org/10.1109/cvpr.2017.277
   Google Scholar
• Ling, X., Liang, J., Wang, D., & Yang, J. (2021, April). A facial expression recognition system for smart learning based on YOLO and vision transformer. In 2021 7th International Conference on Computing and Artificial Intelligence (pp. 178–182). https://doi.org/10.1145/3467707.3467733
   Google Scholar
• Liu, J. L., Liu, B., & Zhang, W. L. (2019). The influences of academic emotions on collaborative problem solving of online learners. China Educational Technology, 390(07), 82–90. https://doi.org/10.3969/j.issn.1006-9860.2019.07.012
   Google Scholar
• Liying, Z., Shijie, S., ZiWen, Q., Huizhen, Y., Guoxiang, Y., & Weiguo, Y. (2020). Intelligent monitoring system based on pain expression recognition. Foreign Electronic Measurement Technology, 39(3), 148–151. https://doi.org/10.19652/j.cnki.femt.201901907
   Google Scholar
• Lu, G., Xie, K., & Liu, Q. (2022). What influences student situational engagement in smart classrooms: Perception of the learning environment and students’ motivation. British Journal of Educational Technology, 53(6), 1665–1687. https://doi.org/10.1111/bjet.13204
   Web of Science ®Google Scholar
• Madrid, H. P., Barros, E., & Vasquez, C. A. (2020). The emotion regulation roots of job satisfaction. Frontiers in Psychology, 11, 609933. https://doi.org/10.3389/fpsyg.2020.609933
   PubMed Web of Science ®Google Scholar
• Meena, G., Mohbey, K. K., & Kumar, S. (2023). Sentiment analysis on images using convolutional neural networks based Inception-V3 transfer learning approach. International Journal of Information Management Data Insights, 3(1), 100174. https://doi.org/10.1016/j.jjimei.2023.100174
   Google Scholar
• Meena, G., Mohbey, K. K., Indian, A., Khan, M. Z., & Kumar, S. (2023). Identifying emotions from facial expressions using a deep convolutional neural network-based approach. Multimedia Tools and Applications, 83(6), 15711–15732. https://doi.org/10.1007/s11042-023-16174-3
   Google Scholar
• Meena, G., Mohbey, K. K., Kumar, S., Chawda, R. K., & Gaikwad, S. V. (2023). Image-based sentiment analysis using InceptionV3 transfer learning approach. SN Computer Science, 4(3), 242. https://doi.org/10.1007/s42979-023-01695-3
   Google Scholar
• Mehendale, N. (2020). Facial emotion recognition using convolutional neural networks (FERC). SN Applied Sciences, 2(3), 446. https://doi.org/10.1007/s42452-020-2234-1
   Web of Science ®Google Scholar
• Minaee, S., Luo, P., Lin, Z., & Bowyer, K. (2021). Going deeper into face detection: A survey. arXiv Preprint arXiv:2103.14983.
   Google Scholar
• Moolchandani, M., Dwivedi, S., Nigam, S., & Gupta, K. (2021, April). A survey on: Facial emotion recognition and classification. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1677–1686). IEEE. https://doi.org/10.1109/ICCMC51019.2021.9418349
   Google Scholar
• Mukhopadhyay, M., Pal, S., Nayyar, A., Pramanik, P. K. D., Dasgupta, N., & Choudhury, P. (2020, February). Facial emotion detection to assess Learner’s State of mind in an online learning system. In Proceedings of the 2020 5th International Conference on Intelligent Information Technology (pp. 107–115). https://doi.org/10.1145/3385209.3385231
   Google Scholar
• Olderbak, S., Wilhelm, O., Hildebrandt, A., & Quoidbach, J. (2019). Sex differences in facial emotion perception ability across the lifespan. Cognition & Emotion, 33(3), 579–588. https://doi.org/10.1080/02699931.2018.1454403
   PubMed Web of Science ®Google Scholar
• Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020). Facial sentiment analysis using AI techniques: State-of-the-art, taxonomies, and challenges. IEEE Access, 8, 90495–90519. https://doi.org/10.1109/ACCESS.2020.2993803
   Web of Science ®Google Scholar
• Putro, M. D., Nguyen, D. L., & Jo, K. H. (2022). A fast CPU real-time facial expression detector using sequential attention network for human–robot interaction. IEEE Transactions on Industrial Informatics, 18(11), 7665–7674. https://doi.org/10.1109/TII.2022.3145862
   Web of Science ®Google Scholar
• Qi, D., Tan, W., Yao, Q., & Liu, J. (2022, October). YOLO5Face: Why reinventing a face detector. In European Conference on Computer Vision (pp. 228–244). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-25072-9_15
   Google Scholar
• Qi, Y., Zhou, C., & Chen, Y. (2023). NA-Resnet: Neighbor block and optimized attention module for global-local feature extraction in facial expression recognition. Multimedia Tools and Applications, 82(11), 16375–16393. https://doi.org/10.1007/s11042-022-14191-2
   Web of Science ®Google Scholar
• Salazar Kämpf, M., Adam, L., Rohr, M. K., Exner, C., & Wieck, C. (2023). A meta-analysis of the relationship between emotion regulation and social affect and cognition. Clinical Psychological Science, 11(6), 1159–1189. https://doi.org/10.1177/21677026221149953
   Web of Science ®Google Scholar
• Sanchez-Moreno, A. S., Olivares-Mercado, J., Hernandez-Suarez, A., Toscano-Medina, K., Sanchez-Perez, G., & Benitez-Garcia, G. (2021). Efficient face recognition system for operating in unconstrained environments. Journal of Imaging, 7(9), 161. https://doi.org/10.3390/jimaging7090161
   PubMed Web of Science ®Google Scholar
• Saxena, S., Tripathi, S., & Sudarshan, T. S. B. (2019, November). Deep dive into faces: Pose & illumination invariant multi-face emotion recognition system. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1088–1093). IEEE. https://doi.org/10.1109/IROS40897.2019.8967874
   Google Scholar
• Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 815–823). https://doi.org/10.1109/cvpr.2015.7298682
   Google Scholar
• Sohail, M., Ali, G., Rashid, J., Ahmad, I., Almotiri, S. H., AlGhamdi, M. A., Nagra, A. A., & Masood, K. (2021). Racial identity-aware facial expression recognition using deep convolutional neural networks. Applied Sciences, 12(1), 88. https://doi.org/10.3390/app12010088
   Google Scholar
• Starr, K., & Braun, S. (2020). Audio description 2.0: Re-versioning audiovisual accessibility to assist emotion recognition. In Innovation in audio description research (pp. 97–120). Routledge.
   Google Scholar
• Suk, M., & Prabhakaran, B. (2014). Real-time mobile facial expression recognition system–A case study. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 132–137). https://doi.org/10.1109/cvprw.2014.25
   Google Scholar
• Sun, W., Li, Y., Tian, F., Fan, X., & Wang, H. (2019). How presenters perceive and react to audience flow prediction in-situ: An explorative study of live online lectures. Proceedings of the ACM on Human–Computer Interaction, 3(CSCW), 1–19. https://doi.org/10.1145/3359264
   PubMedGoogle Scholar
• Ulandari, N., & Lubis, R. K. (2020). The effect of giving discounts and service quality on sales at PT Midi Utama Indonesia Tbk Alfamidi Branch of SM Raja 4 Medan. Journal of Economics and Business, 2(1), 91–96. https://doi.org/10.58471/jecombi.v2i1.12
   Google Scholar
• Wang, K., Peng, X., Yang, J., Lu, S., & Qiao, Y. (2020). Suppressing uncertainties for large-scale facial expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6897–6906). https://doi.org/10.1109/cvpr42600.2020.00693
   Google Scholar
• Wang, W., Xu, K., Niu, H., & Miao, X. (2020). Emotion recognition of students based on facial expressions in online education based on the perspective of computer simulation. Complexity, 2020, 1–9. https://doi.org/10.1155/2020/4065207
   Web of Science ®Google Scholar
• Xia, X., Yang, L., Wei, X., Sahli, H., & Jiang, D. (2022). A multi-scale multi-attention network for dynamic facial expression recognition. Multimedia Systems, 28(2), 479–493. https://doi.org/10.1007/s00530-021-00849-8
   Web of Science ®Google Scholar
• Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). Wider face: A face detection benchmark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5525–5533). https://doi.org/10.1109/cvpr.2016.596
   Google Scholar
• Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., & Wang, X. (2022). Yolo-facev2: A scale and occlusion aware face detector. arXiv Preprint arXiv:2208.02019.
   Google Scholar
• Zapf, D., Kern, M., Tschan, F., Holman, D., & Semmer, N. K. (2021). Emotion work: A work psychology perspective. Annual Review of Organizational Psychology and Organizational Behavior, 8(1), 139–172. https://doi.org/10.1146/annurev-orgpsych-012420-062451
   Google Scholar
• Zhang, J. (2023). Intercultural communication dilemma and countermeasures in international trade. Frontiers in Business, Economics and Management, 10(3), 114–118. https://doi.org/10.54097/fbem.v10i3.11461
   Google Scholar