Abstract
In this paper we address the group-level emotion classification problem in video analytic systems. We propose to apply the MTCNN face detector to obtain facial regions on each video frame. Next, off-the-shelf image features are extracted from each located face using preliminary trained convolutional neural networks. The features of the whole frame are computed as a mean average of image embeddings of individual faces. The resulted frame features are recognized with an ensemble of state-of-the-art classifiers computed as a weighted sum of their outputs. Experimental results with EmotiW 2017 dataset demonstrate that the proposed approach is 2–20% more accurate when compared to the conventional group-level emotion classifiers.
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
References
Krakovsky, M.: Artificial (emotional) intelligence. Commun. ACM 61(4), 18–19 (2018)
Dhall, A., Goecke, R., Ghosh, S., Joshi, J., Hoey, J., Gedeon, T.: From individual to group-level emotion recognition: EmotiW 5.0. In: 19th ACM International Conference on Multimodal Interaction (ICMI), pp. 524–528. ACM (2017)
Vielzeuf, V., Pateux, S., Jurie, F.: Temporal multimodal fusion for video emotion classification in the wild. In: 19th ACM International Conference on Multimodal Interaction (ICMI), pp. 569–576. ACM (2017)
Fan, Y., Lu, X., Li, D., Liu Y.: Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: 18th ACM International Conference on Multimodal Interaction (ICMI), pp. 445–450. ACM (2016)
Surace, L., Patacchiola, M., Sönmez, E.B., Spataro, W., Cangelosi, A.: Emotion recognition in the wild using deep neural networks and Bayesian classifiers. In: 19th ACM International Conference on Multimodal Interaction (ICMI), pp. 593–597. ACM (2017)
Kaya, H., Gürpınar, F., Salah, A.A.: Video-based emotion recognition in the wild using deep transfer learning and score fusion. Image Vis. Comput. 65, 66–75 (2017)
Rassadin, A., Gruzdev, A., Savchenko, A.: Group-level emotion recognition using transfer learning from face identification. In: 19th ACM International Conference on Multimodal Interaction (ICMI), pp. 544–548. ACM (2017)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Savchenko, A.V., Belova, N.S., Savchenko, L.V.: Fuzzy analysis and deep convolution neural networks in still-to-video recognition. Opt. Mem. Neural Netw. (Inf. Opt.) 27(1), 23–31 (2018)
Hu, P., Ramanan, D.: Finding tiny faces. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1522–1530. IEEE (2017)
Arriaga, O., Valdenegro-Toro, M., Plöger, P.: Real-time convolutional neural networks for emotion and gender classification. arXiv preprint arXiv:1710.07557 (2017)
Guo, X., Polania, L., Barner, K.: Group-level emotion recognition using deep models on image scene, faces, and skeletons. In: 19th ACM International Conference on Multimodal Interaction (ICMI), pp. 603–608. ACM (2017)
Rassadin, A.G., Savchenko, A.V.: Compressing deep convolutional neural networks in visual emotion recognition. In: Proceedings of the International Conference on Information Technology and Nanotechnology (ITNT). Session Image Processing, Geoinformation Technology and Information Security Image Processing (IPGTIS), CEUR-WS, vol. 1901, pp. 207–213 (2017)
Acknowledgements
The article was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2017 (grant №17-05-0007) and by the Russian Academic Excellence Project “5-100”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Tarasov, A.V., Savchenko, A.V. (2018). Emotion Recognition of a Group of People in Video Analytics Using Deep Off-the-Shelf Image Embeddings. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2018. Lecture Notes in Computer Science(), vol 11179. Springer, Cham. https://doi.org/10.1007/978-3-030-11027-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-11027-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11026-0
Online ISBN: 978-3-030-11027-7
eBook Packages: Computer ScienceComputer Science (R0)