Skip to main content
SpringerLink
Log in

Group-Level Affect Recognition in Video Using Deviation of Frame Features

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13217))

  • 363 Accesses

Abstract

In this paper, we propose the novel video-based group-level emotion recognition algorithm. At first, the faces are detected in each video frame, and their features are extracted using a lightweight neural network, e.g., MobileNet pre-trained on large emotional dataset, such as AffectNet. The frame descriptor is defined as a concatenation of STAT features (max, average, standard deviation, etc.). The descriptor of the whole video is computed as a deviation of the frame descriptors, and the resulting video features are fed into a classifier. Experimental results for the VGAF dataset from the EmotiW 2020 challenge demonstrate that the proposed approach has 1% greater accuracy than the best-known single model. It is also at least 5% better than any other facial processing technique. Moreover, a blending of facial expression recognition with a processing of audio features extracted by the OpenSMILE library is comparable with the best-known ensemble for this dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/HSE-asavchenko/face-emotion-recognition.

References

  1. Veltmeijer, E.A., Gerritsen, C., Hindriks, K.: Automatic emotion recognition for groups: a review. IEEE Trans. Affect. Comput. (2021)

    Google Scholar 

  2. Sharma, G., Dhall, A., Cai, J.: Audio-visual automatic group affect analysis. IEEE Trans. Affect. Comput. (2021)

    Google Scholar 

  3. Pinto, J.R., et al.: Audiovisual classification of group emotion valence using activity recognition networks. In: Proceedings of the 4th International Conference on Image Processing, Applications and Systems (IPAS), pp. 114–119. IEEE (2020)

    Google Scholar 

  4. Wang, Y., Wu, J., Heracleous, P., Wada, S., Kimura, R., Kurihara, S.: Implicit knowledge injectable cross attention audiovisual model for group emotion recognition. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 827–834 (2020)

    Google Scholar 

  5. Sun, M., et al.: Multi-modal fusion using spatio-temporal and static features for group emotion recognition. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 835–840 (2020)

    Google Scholar 

  6. Petrova, A., Vaufreydaz, D., Dessus, P.: Group-level emotion recognition using a unimodal privacy-safe non-individual approach. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 813–820 (2020)

    Google Scholar 

  7. Liu, C., Jiang, W., Wang, M., Tang, T.: Group level audio-video emotion recognition using hybrid networks. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 807–812 (2020)

    Google Scholar 

  8. Savchenko, A.V.: Maximum-likelihood dissimilarities in image recognition with deep neural networks. Comput. Opt. 41(3), 422–430 (2017)

    Article  Google Scholar 

  9. Savchenko, A.V.: Probabilistic neural network with complex exponential activation functions in image recognition. IEEE Trans. Neural Netw. Learn. Syst. 31(2), 651–660 (2020)

    Article  MathSciNet  Google Scholar 

  10. Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: Proceedings of International Conference on Automatic Face & Gesture Recognition (FG), pp. 67–74. IEEE (2018)

    Google Scholar 

  11. Mollahosseini, A., Hasani, B., Mahoor, M.H.: AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18–31 (2017)

    Article  Google Scholar 

  12. Savchenko, A.V.: Facial expression and attributes recognition based on multi-task learning of lightweight neural networks. In: Proceedings of the 19th International Symposium on Intelligent Systems and Informatics (SISY), pp. 119–124. IEEE (2021)

    Google Scholar 

  13. Bargal, S.A., Barsoum, E., Ferrer, C.C., Zhang, C.: Emotion recognition in the wild from videos using images. In: Proceedings of the ACM International Conference on Multimodal Interaction (ICMI), pp. 433–436 (2016)

    Google Scholar 

  14. Knyazev, B., Shvetsov, R., Efremova, N., Kuharenko, A.: Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video. arXiv preprint arXiv:1711.04598 (2017)

  15. Savchenko, L., V. Savchenko, A.: Speaker-aware training of speech emotion classifier with speaker recognition. In: Karpov, A., Potapova, R. (eds.) SPECOM 2021. LNCS (LNAI), vol. 12997, pp. 614–625. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87802-3_55

    Chapter  Google Scholar 

  16. Demochkina, P., Savchenko, A.V.: MobileEmotiFace: efficient facial image representations in video-based emotion recognition on mobile devices. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12665, pp. 266–274. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68821-9_25

    Chapter  Google Scholar 

  17. Lomotin, K., Makarov, I.: Automated image and video quality assessment for computational video editing. In: van der Aalst, W.M.P., et al. (eds.) AIST 2020. LNCS, vol. 12602, pp. 243–256. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72610-2_18

    Chapter  Google Scholar 

  18. Zuenko, D., Makarov, I.: Style-transfer autoencoder for efficient deep voice conversation. In: Proceedings of the International Symposium on Computational Intelligence and Informatics (CINTI), pp. 41–6. IEEE (2021)

    Google Scholar 

  19. Savchenko, A.V.: Phonetic words decoding software in the problem of Russian speech recognition. Autom. Remote. Control. 74(7), 1225–1232 (2013)

    Article  Google Scholar 

  20. Eyben, F., Wöllmer, M., Schuller, B.: OpenSMILE: the Munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459–1462 (2010)

    Google Scholar 

  21. Schuller, B., et al.: The INTERSPEECH 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism. In: Proceedings of 14th Annual Conference of the International Speech Communication Association (INTERSPEECH) (2013)

    Google Scholar 

  22. Savchenko, A.V., Savchenko, V.V.: A method for measuring the pitch frequency of speech signals for the systems of acoustic speech analysis. Meas. Tech. 62(3), 282–288 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by RSF (Russian Science Foundation) grant 20-71-10010.

Author information

Authors and Affiliations

  1. HSE University, Laboratory of Algorithms and Technologies for Network Analysis, Nizhny Novgorod, Russia

    Andrey V. Savchenko & Lyudmila V. Savchenko

  2. HSE University, Moscow, Russia

    Natalya S. Belova

Authors
  1. Andrey V. Savchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lyudmila V. Savchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natalya S. Belova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrey V. Savchenko .

Editor information

Editors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Evgeny Burnaev

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Skolkovo Institute of Science and Technology, Moscow, Russia

    Sergei Ivanov

  4. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrei Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  9. LORIA, Campus Scientifique, Vandœuvre lès Nancy, France

    Amedeo Napoli

  10. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Aalto University, Espoo, Finland

    Jari Saramäki

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Yandex LLC, Moscow, Russia

    Evgenii Tsymbalov

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Savchenko, A.V., Savchenko, L.V., Belova, N.S. (2022). Group-Level Affect Recognition in Video Using Deviation of Frame Features. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16500-9_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16499-6

  • Online ISBN: 978-3-031-16500-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation