A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

Facial expression is one of the important ways of transferring emotion in interpersonal communication, and it has been widely used in many interpersonal communication systems. The traditional facial expression recognition methods are not intelligent enough to manage the model uncertainty. The deep l...

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Autores principales:	Dong Zhang, Qichuan Tian
Formato:	article
Lenguaje:	EN
Publicado:	Kaunas University of Technology 2021
Materias:	facial expression cnn fuzzy optimized cnn-rnn uncertainty reduction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/42413a0cc0aa4993b23d8615e5191986
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id	oai:doaj.org-article:42413a0cc0aa4993b23d8615e5191986
record_format	dspace
spelling	oai:doaj.org-article:42413a0cc0aa4993b23d8615e51919862021-11-04T14:17:16ZA Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition1392-12152029-573110.5755/j02.eie.29648https://doaj.org/article/42413a0cc0aa4993b23d8615e51919862021-10-01T00:00:00Zhttps://eejournal.ktu.lt/index.php/elt/article/view/29648https://doaj.org/toc/1392-1215https://doaj.org/toc/2029-5731Facial expression is one of the important ways of transferring emotion in interpersonal communication, and it has been widely used in many interpersonal communication systems. The traditional facial expression recognition methods are not intelligent enough to manage the model uncertainty. The deep learning method has obvious ability to deal with model uncertainty in the image recognition. The deep learning method is able to complete the facial expression work, but the recognition rate can be further improved by a hybrid learning strategy. In this paper, a Fuzzy optimized convolutional neural network-recurrent neural network (CNN-RNN) method for facial expression recognition is proposed to solve the problems of direct image convolution without image enhancement and simple convolution stack ignoring feature layer-by-layer convolution resulting in information loss. Firstly, each face image is scaled by the bilinear interpolation and the affine transformation is adopted to expand the image data to avoid the shortage of the data set. Then the feature map of the facial expression is extracted by the CNN with small information loss. To deal with the uncertainty in the feature map, the Fuzzy logic is employed to reduce the uncertainty by recognizing the highly nonlinear relationship between the features. Then the output of the Fuzzy model is fed with the RNN to classify different facial expression images. The recognition results based on the open datasets CK, Jaffe, and FER2013 show that the proposed Fuzzy optimized CNN-RNN method has a certain improvement in the recognition effect of different facial expression data sets compared with current popular algorithms.Dong ZhangQichuan TianKaunas University of Technologyarticlefacial expressioncnnfuzzy optimized cnn-rnnuncertainty reductionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElektronika ir Elektrotechnika, Vol 27, Iss 5, Pp 67-74 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	facial expression cnn fuzzy optimized cnn-rnn uncertainty reduction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	facial expression cnn fuzzy optimized cnn-rnn uncertainty reduction Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dong Zhang Qichuan Tian A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition
description	Facial expression is one of the important ways of transferring emotion in interpersonal communication, and it has been widely used in many interpersonal communication systems. The traditional facial expression recognition methods are not intelligent enough to manage the model uncertainty. The deep learning method has obvious ability to deal with model uncertainty in the image recognition. The deep learning method is able to complete the facial expression work, but the recognition rate can be further improved by a hybrid learning strategy. In this paper, a Fuzzy optimized convolutional neural network-recurrent neural network (CNN-RNN) method for facial expression recognition is proposed to solve the problems of direct image convolution without image enhancement and simple convolution stack ignoring feature layer-by-layer convolution resulting in information loss. Firstly, each face image is scaled by the bilinear interpolation and the affine transformation is adopted to expand the image data to avoid the shortage of the data set. Then the feature map of the facial expression is extracted by the CNN with small information loss. To deal with the uncertainty in the feature map, the Fuzzy logic is employed to reduce the uncertainty by recognizing the highly nonlinear relationship between the features. Then the output of the Fuzzy model is fed with the RNN to classify different facial expression images. The recognition results based on the open datasets CK, Jaffe, and FER2013 show that the proposed Fuzzy optimized CNN-RNN method has a certain improvement in the recognition effect of different facial expression data sets compared with current popular algorithms.
format	article
author	Dong Zhang Qichuan Tian
author_facet	Dong Zhang Qichuan Tian
author_sort	Dong Zhang
title	A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition
title_short	A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition
title_full	A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition
title_fullStr	A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition
title_full_unstemmed	A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition
title_sort	novel fuzzy optimized cnn-rnn method for facial expression recognition
publisher	Kaunas University of Technology
publishDate	2021
url	https://doaj.org/article/42413a0cc0aa4993b23d8615e5191986
work_keys_str_mv	AT dongzhang anovelfuzzyoptimizedcnnrnnmethodforfacialexpressionrecognition AT qichuantian anovelfuzzyoptimizedcnnrnnmethodforfacialexpressionrecognition AT dongzhang novelfuzzyoptimizedcnnrnnmethodforfacialexpressionrecognition AT qichuantian novelfuzzyoptimizedcnnrnnmethodforfacialexpressionrecognition
_version_	1718444832906543104

A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition

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