Emotion Recognition using AutoEncoders and Convolutional Neural Networks
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,...
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Brno University of Technology
2018-06-01
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Online Access: | https://mendel-journal.org/index.php/mendel/article/view/31 |
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doaj-d987f1fcc27844a1a74a5330d77f74252021-07-21T07:38:40ZengBrno University of TechnologyMendel1803-38142571-37012018-06-0124110.13164/mendel.2018.1.11331Emotion Recognition using AutoEncoders and Convolutional Neural NetworksLuis Antonio Beltrán PrietoZuzana Kominkova Oplatkova 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%. https://mendel-journal.org/index.php/mendel/article/view/31Emotion RecognitionConvolutional Neural NetworksDeep LearningAutoEncoders |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Luis Antonio Beltrán Prieto Zuzana Kominkova Oplatkova |
spellingShingle |
Luis Antonio Beltrán Prieto Zuzana Kominkova Oplatkova Emotion Recognition using AutoEncoders and Convolutional Neural Networks Mendel Emotion Recognition Convolutional Neural Networks Deep Learning AutoEncoders |
author_facet |
Luis Antonio Beltrán Prieto Zuzana Kominkova Oplatkova |
author_sort |
Luis Antonio Beltrán Prieto |
title |
Emotion Recognition using AutoEncoders and Convolutional Neural Networks |
title_short |
Emotion Recognition using AutoEncoders and Convolutional Neural Networks |
title_full |
Emotion Recognition using AutoEncoders and Convolutional Neural Networks |
title_fullStr |
Emotion Recognition using AutoEncoders and Convolutional Neural Networks |
title_full_unstemmed |
Emotion Recognition using AutoEncoders and Convolutional Neural Networks |
title_sort |
emotion recognition using autoencoders and convolutional neural networks |
publisher |
Brno University of Technology |
series |
Mendel |
issn |
1803-3814 2571-3701 |
publishDate |
2018-06-01 |
description |
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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topic |
Emotion Recognition Convolutional Neural Networks Deep Learning AutoEncoders |
url |
https://mendel-journal.org/index.php/mendel/article/view/31 |
work_keys_str_mv |
AT luisantoniobeltranprieto emotionrecognitionusingautoencodersandconvolutionalneuralnetworks AT zuzanakominkovaoplatkova emotionrecognitionusingautoencodersandconvolutionalneuralnetworks |
_version_ |
1721293001857171456 |