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Group-Level Affect Recognition in Video Using Deviation of Frame Features
Ch. 13217. P. 199–207.
A. V. Savchenko, L. V. Savchenko, Information Sciences 2023 Vol. 648 Article 119540
This article introduces the novel technique to reduce the computation time for classifying a sequence of observations (frames), such as a video stream, where each observation is described by high-dimensional embeddings extracted by a deep neural network. By using the methodology of granular computing, an observed sequence is represented at various scales using different frame ...
Added: August 27, 2023
Churaev E., Savchenko A., Software Impacts 2023 Vol. 16 Article 100507
Nowadays, many meetings, lessons, conferences, and presentations are organized online, where it is complicated to communicate with an audience and control their engagement and emotions. In this article, we present a novel C++ application that is led to help estimate facial identities and expressions. It captures a screen with a window of an arbitrary online ...
Added: May 18, 2023
Savchenko A., / Series Computer Science "arxiv.org". 2022.
In this paper, we present the results of the HSE-NN team in the 4th competition on Affective Behavior Analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward ...
Added: October 21, 2022
Savchenko A., , in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).: IEEE, 2022. P. 2358–2365.
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on Affect-Net. The predictions for sequential frames are smoothed ...
Added: August 29, 2022
Savchenko A., Savchenko L., Makarov I., IEEE Transactions on Affective Computing 2022 Vol. 13 No. 4 P. 2132–2143
In this paper, behaviour of students in the e-learning environment is analyzed. The novel pipeline is proposed based on video facial processing. At first, face detection, tracking and clustering techniques are applied to extract the sequences of faces of each student. Next, a single efficient neural network is used to extract emotional features in each ...
Added: July 14, 2022
Лунякова Е. Г., Гани-Заде Д. С., The Russian Journal of Cognitive Science 2019 Vol. 6 No. 3 P. 6–13
The present research focuses on the mechanisms of facial expression recognition. We explored the relationship between eye movement strategies in face perception processes and the intensity of holistic perception effects — namely, the inversion effect. It was assumed that if holistic and feature-based mechanisms rely on certain specific image viewing strategies, the intensity of the ...
Added: December 10, 2020
Springer, 2020.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, ...
Added: October 27, 2019
Tarasov Alexander V., Savchenko A., , in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer ScienceVol. 11179.: Berlin: Springer, 2018. Ch. 19 P. 191–198.
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 ...
Added: December 12, 2018
Alexandr Rassadin, Alexey Gruzdev, Andrey Savchenko, , in: Proceedings of the 19th ACM International Conference on Multimodal Interaction.: [б.и.], 2017. P. 544–548.
In this paper we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final ...
Added: October 18, 2017
Alexandr Rassadin, Alexey Gruzdev, Andrey Savchenko, / Series cs.CV "Computer Science > Computer Vision and Pattern Recognition". 2017. No. 1709.01688.
In this paper we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final ...
Added: October 17, 2017