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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Veltmeijer, E.A., Gerritsen, C., Hindriks, K.: Automatic emotion recognition for groups: a review. IEEE Trans. Affect. Comput. (2021)
Sharma, G., Dhall, A., Cai, J.: Audio-visual automatic group affect analysis. IEEE Trans. Affect. Comput. (2021)
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)
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)
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)
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)
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)
Savchenko, A.V.: Maximum-likelihood dissimilarities in image recognition with deep neural networks. Comput. Opt. 41(3), 422–430 (2017)
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)
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)
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)
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)
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)
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)
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
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
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
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)
Savchenko, A.V.: Phonetic words decoding software in the problem of Russian speech recognition. Autom. Remote. Control. 74(7), 1225–1232 (2013)
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)
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)
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)
Acknowledgements
The work is supported by RSF (Russian Science Foundation) grant 20-71-10010.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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)