Emotion Recognition using AutoEncoders and Convolutional Neural Networks
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Date
2018-06-01
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Advisor
Referee
Mark
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Volume Title
Publisher
Institute of Automation and Computer Science, Brno University of Technology
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Abstract
Emotions demonstrate people's reactions to certain stimuli. Facial expression analysis is often used to identify the emotion expressed. Machine learning algorithms combined with artificial intelligence techniques have been developed in order to detect expressions found in multimedia elements, including videos and pictures. Advanced methods to achieve this include the usage of Deep Learning algorithms. The aim of this paper is to analyze the performance of a Convolutional Neural Network which uses AutoEncoder Units for emotion-recognition in human faces. The combination of two Deep Learning techniques boosts the performance of the classification system. 8000 facial expressions from the Radboud Faces Database were used during this research for both training and testing. The outcome showed that five of the eight analyzed emotions presented higher accuracy rates, higher than 90%.
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Citation
Mendel. 2018 vol. 24, č. 1, s. 113-120. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/31
https://mendel-journal.org/index.php/mendel/article/view/31
Document type
Peer-reviewed
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Published version
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en