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Emotion Recognition for Multiple Context Awareness

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Understanding emotion in context is a rising hotspot in the computer vision community. Existing methods lack reliable context semantics to mitigate uncertainty in expressing emotions and fail to model multiple context representations complementarily. To alleviate these issues, we present a context-aware emotion recognition framework that combines four complementary contexts. The first context is multimodal emotion recognition based on facial expression, facial landmarks, gesture and gait. Secondly, we adopt the channel and spatial attention modules to obtain the emotion semantics of the scene context. Inspired by sociology theory, we explore the emotion transmission between agents by constructing relationship graphs in the third context. Meanwhile, we propose a novel agent-object context, which aggregates emotion cues from the interactions between surrounding agents and objects in the scene to mitigate the ambiguity of prediction. Finally, we introduce an adaptive relevance fusion module for learning the shared representations among multiple contexts. Extensive experiments show that our approach outperforms the state-of-the-art methods on both EMOTIC and GroupWalk datasets. We also release a dataset annotated with diverse emotion labels, Human Emotion in Context (HECO). In practice, we compare with the existing methods on the HECO, and our approach obtains a higher classification average precision of 50.65% and a lower regression mean error rate of 0.7. The project is available at https://heco2022.github.io/.

Di. Yang and S. Huang—Equal contribution.

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References

  1. Baltrušaitis, T., Robinson, P., Morency, L.P.: OpenFace: an open source facial behavior analysis toolkit. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. IEEE (2016)

    Google Scholar 

  2. Barrett, L.F., Mesquita, B., Gendron, M.: Context in emotion perception. Curr. Dir. Psychol. Sci. 20(5), 286–290 (2011)

    Article  Google Scholar 

  3. Bos, D.O., et al.: EEG-based emotion recognition. The influence of visual and auditory stimuli, vol. 56, no. 3, pp. 1–17 (2006)

    Google Scholar 

  4. Calhoun, C., Solomon, R.C.: What is an emotion?: classic readings in philosophical psychology (1984)

    Google Scholar 

  5. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291–7299 (2017)

    Google Scholar 

  6. Castellano, G., Kessous, L., Caridakis, G.: Emotion recognition through multiple modalities: face, body gesture, speech. In: Peter, C., Beale, R. (eds.) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868, pp. 92–103. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85099-1_8

    Chapter  Google Scholar 

  7. Chandra, R., Bhattacharya, U., Roncal, C., Bera, A., Manocha, D.: RobustTP: end-to-end trajectory prediction for heterogeneous road-agents in dense traffic with noisy sensor inputs. In: ACM Computer Science in Cars Symposium, pp. 1–9 (2019)

    Google Scholar 

  8. Chao, Y.W., Liu, Y., Liu, X., Zeng, H., Deng, J.: Learning to detect human-object interactions. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 381–389. IEEE Computer Society (2018)

    Google Scholar 

  9. Chen, Z., Li, B., Xu, J., Wu, S., Ding, S., Zhang, W.: Towards practical certifiable patch defense with vision transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15148–15158 (2022)

    Google Scholar 

  10. Clavel, C., Vasilescu, I., Devillers, L., Richard, G., Ehrette, T.: Fear-type emotion recognition for future audio-based surveillance systems. Speech Commun. 50(6), 487–503 (2008)

    Article  Google Scholar 

  11. Cornelius, R.R.: The Science of Emotion: Research and Tradition in the Psychology of Emotions. Prentice-Hall, Inc., Upper Saddle River (1996)

    Google Scholar 

  12. Cowie, R., et al.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18(1), 32–80 (2001)

    Article  Google Scholar 

  13. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems 29 (2016)

    Google Scholar 

  14. Davidson, R.J., Sherer, K.R., Goldsmith, H.H.: Handbook of Affective Sciences. Oxford University Press, Oxford (2009)

    Google Scholar 

  15. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  16. Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Acted facial expressions in the wild database. Australia, Technical report TR-CS-11 2, 1, Australian National University, Canberra (2011)

    Google Scholar 

  17. Frijda, N.H.: Emotion, cognitive structure, and action tendency. Cogn. Emot. 1(2), 115–143 (1987)

    Article  Google Scholar 

  18. Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8359–8367 (2018)

    Google Scholar 

  19. Gordon, S.L.: The sociology of sentiments and emotion. In: Social psychology, pp. 562–592. Routledge (2017)

    Google Scholar 

  20. Gunes, H., Piccardi, M.: Bi-modal emotion recognition from expressive face and body gestures. J. Netw. Comput. Appl. 30(4), 1334–1345 (2007)

    Article  Google Scholar 

  21. Gupta, S., Malik, J.: Visual semantic role labeling. arXiv preprint arXiv:1505.04474 (2015)

  22. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  23. Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)

    Article  Google Scholar 

  24. Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)

  25. Hoang, M.H., Kim, S.H., Yang, H.J., Lee, G.S.: Context-aware emotion recognition based on visual relationship detection. IEEE Access 9, 90465–90474 (2021)

    Article  Google Scholar 

  26. Huang, H., et al.: CMUA-watermark: a cross-model universal adversarial watermark for combating deepfakes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 1, pp. 989–997 (2022). https://doi.org/10.1609/aaai.v36i1.19982. https://ojs.aaai.org/index.php/AAAI/article/view/19982

  27. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)

    Google Scholar 

  28. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  29. Kopuklu, O., Kose, N., Rigoll, G.: Motion fused frames: data level fusion strategy for hand gesture recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2103–2111 (2018)

    Google Scholar 

  30. Kosti, R., Alvarez, J.M., Recasens, A., Lapedriza, A.: Context based emotion recognition using emotic dataset. IEEE Trans. Pattern Anal. Mach. Intell. 42(11), 2755–2766 (2019)

    Google Scholar 

  31. Lee, J., Kim, S., Kim, S., Park, J., Sohn, K.: Context-aware emotion recognition networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10143–10152 (2019)

    Google Scholar 

  32. Li, Z., Snavely, N.: MegaDepth: learning single-view depth prediction from internet photos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2041–2050 (2018)

    Google Scholar 

  33. Liao, Y., Liu, S., Wang, F., Chen, Y., Qian, C., Feng, J.: PPDM: parallel point detection and matching for real-time human-object interaction detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 482–490 (2020)

    Google Scholar 

  34. Liu, K., Gebraeel, N.Z., Shi, J.: A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Trans. Autom. Sci. Eng. 10(3), 652–664 (2013)

    Article  Google Scholar 

  35. Liu, S., et al.: Efficient universal shuffle attack for visual object tracking. In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2739–2743 (2022). https://doi.org/10.1109/ICASSP43922.2022.9747773

  36. Liu, X., Shi, H., Chen, H., Yu, Z., Li, X., Zhao, G.: iMiGUE: an identity-free video dataset for micro-gesture understanding and emotion analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10631–10642 (2021)

    Google Scholar 

  37. Liu, Y., Liu, J., Zhao, M., Li, S., Song, L.: Collaborative normality learning framework for weakly supervised video anomaly detection. IEEE Trans. Circuits Syst. II Express Briefs 69(5), 2508–2512 (2022). https://doi.org/10.1109/TCSII.2022.3161061

    Article  Google Scholar 

  38. Lu, Y., Zheng, W.L., Li, B., Lu, B.L.: Combining eye movements and EEG to enhance emotion recognition. In: IJCAI, vol. 15, pp. 1170–1176. Citeseer (2015)

    Google Scholar 

  39. Mehrabian, A.: Basic Dimensions for a General Psychological Theory: Implications for Personality, Social, Environmental, and Developmental Studies, vol. 2. Oelgeschlager, Gunn & Hain, Cambridge (1980)

    Google Scholar 

  40. Mesquita, B., Boiger, M.: Emotions in context: a sociodynamic model of emotions. Emot. Rev. 6(4), 298–302 (2014)

    Article  Google Scholar 

  41. Mittal, T., Bera, A., Manocha, D.: Multimodal and context-aware motion perception model with multiplicative fusion. IEEE MultiMedia 28, 67–75 (2021)

    Article  Google Scholar 

  42. Mittal, T., Guhan, P., Bhattacharya, U., Chandra, R., Bera, A., Manocha, D.: Emoticon: Context-aware multimodal emotion recognition using Frege’s principle. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14234–14243 (2020)

    Google Scholar 

  43. Musch, J., Klauer, K.C.: The Psychology of Evaluation: Affective Processes in Cognition and Emotion. Psychology Press, Brighton (2003)

    Book  Google Scholar 

  44. Navarretta, C.: Individuality in communicative bodily behaviours. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) Cognitive Behavioural Systems. LNCS, vol. 7403, pp. 417–423. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34584-5_37

    Chapter  Google Scholar 

  45. Niedenthal, P.M., Ric, F.: Psychology of Emotion. Psychology Press, Brighton (2017)

    Book  Google Scholar 

  46. Niwattanakul, S., Singthongchai, J., Naenudorn, E., Wanapu, S.: Using of Jaccard coefficient for keywords similarity. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 1, pp. 380–384 (2013)

    Google Scholar 

  47. Ochsner, K.N., Gross, J.J.: The cognitive control of emotion. Trends Cogn. Sci. 9(5), 242–249 (2005)

    Article  Google Scholar 

  48. Paszke, A., et al.: Automatic differentiation in PyTorch (2017)

    Google Scholar 

  49. Piana, S., Stagliano, A., Odone, F., Verri, A., Camurri, A.: Real-time automatic emotion recognition from body gestures. arXiv preprint arXiv:1402.5047 (2014)

  50. Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., Morency, L.P.: Context-dependent sentiment analysis in user-generated videos. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 873–883 (2017)

    Google Scholar 

  51. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28, pp. 91–99 (2015)

    Google Scholar 

  52. Rozgić, V., Ananthakrishnan, S., Saleem, S., Kumar, R., Vembu, A.N., Prasad, R.: Emotion recognition using acoustic and lexical features. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)

    Google Scholar 

  53. Ruckmick, C.A.: The psychology of feeling and emotion (1936)

    Google Scholar 

  54. Schachter, S., Singer, J.: Cognitive, social, and physiological determinants of emotional state. Psychol. Rev. 69(5), 379 (1962)

    Article  Google Scholar 

  55. Sikka, K., Dykstra, K., Sathyanarayana, S., Littlewort, G., Bartlett, M.: Multiple kernel learning for emotion recognition in the wild. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 517–524 (2013)

    Google Scholar 

  56. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  57. Stets, J.E.: Current emotion research in sociology: advances in the discipline. Emot. Rev. 4(3), 326–334 (2012)

    Article  Google Scholar 

  58. Tsai, Y.H.H., Bai, S., Liang, P.P., Kolter, J.Z., Morency, L.P., Salakhutdinov, R.: Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the Conference Meeting on Association for Computational Linguistics, vol. 2019, p. 6558. NIH Public Access (2019)

    Google Scholar 

  59. Tsai, Y.H.H., Liang, P.P., Zadeh, A., Morency, L.P., Salakhutdinov, R.: Learning factorized multimodal representations. arXiv preprint arXiv:1806.06176 (2018)

  60. Ulutan, O., Iftekhar, A., Manjunath, B.S.: VSGNet: spatial attention network for detecting human object interactions using graph convolutions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13617–13626 (2020)

    Google Scholar 

  61. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  62. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  63. Wallbott, H.G.: Bodily expression of emotion. European J. Soc. Psychol. 28(6), 879–896 (1998)

    Article  Google Scholar 

  64. Wang, S., Yang, D., Zhai, P., Chen, C., Zhang, L.: TSA-NET: tube self-attention network for action quality assessment. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4902–4910 (2021)

    Google Scholar 

  65. Wang, W., et al.: Comp-GAN: compositional generative adversarial network in synthesizing and recognizing facial expression. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 211–219 (2019)

    Google Scholar 

  66. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1

    Chapter  Google Scholar 

  67. Xie, S., Hu, H., Wu, Y.: Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recogn. 92, 177–191 (2019)

    Article  Google Scholar 

  68. Xiu, Y., Li, J., Wang, H., Fang, Y., Lu, C.: Pose Flow: efficient online pose tracking. In: BMVC (2018)

    Google Scholar 

  69. Yeh, H., Curtis, S., Patil, S., van den Berg, J., Manocha, D., Lin, M.: Composite agents. In: Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 39–47 (2008)

    Google Scholar 

  70. Zadeh, A., Zellers, R., Pincus, E., Morency, L.P.: Multimodal sentiment intensity analysis in videos: facial gestures and verbal messages. IEEE Intell. Syst. 31(6), 82–88 (2016)

    Article  Google Scholar 

  71. Zhai, P., Luo, J., Dong, Z., Zhang, L., Wang, S., Yang, D.: Robust adversarial reinforcement learning with dissipation inequation constraint (2022)

    Google Scholar 

  72. Zhang, M., Liang, Y., Ma, H.: Context-aware affective graph reasoning for emotion recognition. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 151–156. IEEE (2019)

    Google Scholar 

  73. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Analy. Mach. Intell. 40(6), 1452–1464 (2017)

    Article  Google Scholar 

  74. Zhu, J., Luo, B., Zhao, S., Ying, S., Zhao, X., Gao, Y.: IExpressNet: facial expression recognition with incremental classes. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2899–2908 (2020)

    Google Scholar 

  75. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2879–2886. IEEE (2012)

    Google Scholar 

  76. Ziemke, T.: On the role of emotion in biological and robotic autonomy. BioSystems 91(2), 401–408 (2008)

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Key R&D Program of China (2021ZD0113502, 2021ZD0113503), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0103) and National Natural Science Foundation of China under Grant (82090052).

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Authors and Affiliations

  1. Academy for Engineering and Technology, Fudan University, Shanghai, China

    Dingkang Yang, Shuai Huang, Shunli Wang, Yang Liu, Peng Zhai, Liuzhen Su, Mingcheng Li & Lihua Zhang

  2. Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China

    Dingkang Yang, Shuai Huang, Shunli Wang, Peng Zhai, Liuzhen Su, Mingcheng Li & Lihua Zhang

  3. Jilin Provincial Key Laboratory of Intelligence Science and Engineering, Changchun, China

    Lihua Zhang

  4. AI and Unmanned Systems Engineering Research Center of Jilin Province, Changchun, China

    Lihua Zhang

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  1. Dingkang Yang
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Correspondence to Lihua Zhang .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Yang, D. et al. (2022). Emotion Recognition for Multiple Context Awareness. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13697. Springer, Cham. https://doi.org/10.1007/978-3-031-19836-6_9

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