In this paper we study the problem of predicting the cohesiveness and emotion of a group of people in photo. We proposed a fast approach, consisting of face detection by using MTCNN, aggregation of facial features (ag...
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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...
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In this paper, we propose to solve the problem of facial expression recognition in videos by implementing a two-stage procedure, in which, firstly, facial features are extracted from all frames using an EfficientNet-b...
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In this paper, we propose a novel video summarization technique for automatic affect analysis of participants of an online event. At first, face verification neural network is used to cluster facial regions that corre...
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In this paper, we examine the issue of video-based facial emotion recognition algorithms which show excellent performance on some benchmarks, but have much worse accuracy in practical applications. For example, the ty...
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We analyzed the way to increase computational efficiency of video-based image recognition methods with matching of high dimensional feature vectors extracted by deep convolutional neural networks. We proposed an algor...
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In this article, the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction are used for downstream emotion analysis tasks from the fifth Affective Behavior analysis in-the-w...
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In this paper, we present the novel multi-task EfficientNet model and its usage in the 4th competition on Affective Behavior analysis in-the-wild (ABAW). This model is trained for simultaneous recognition of facial ex...
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In this paper, we explore the possibility to improve efficiency of face recognition using information about anomaly input images. Indeed, modern publicly-available datasets typically contain images of mostly middle-ag...
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Problem of learning a graphical model (graphical model selection problem) consists of recovering a conditional dependence structure (concentration graph) from data given as a sample of observations from a random vecto...
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