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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Aleksandr Tarasov
Deep Learning Technologies in Group Emotion Monitoring for Video Analytics System
Business Informatics
(Bachelor’s programme)
2018
In this paper the group-level emotion classification problem in video analytic systems of supermarkets is addressed. The requirements for video analytic system for the marketing needs are gathered. New algorithm for group-level emotion recognition is proposed. MTCNN face detector is applied 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 achieves 75.5% accuracy which is 8-23% more accurate when compared to the conventional group-level emotion classifiers. Program module is implemented according to the requirements.

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