Deep Learning for Emotion Recognition in Faces

Ariel Ruiz-Garcia; Mark Elshaw; Abdulrahman Altahhan; Vasile Palade

doi:10.1007/978-3-319-44781-0_5

Deep Learning for Emotion Recognition in Faces

Ariel Ruiz-Garcia, Mark Elshaw, Abdulrahman Altahhan, Vasile Palade

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

26 Citations (Scopus)

Abstract

Deep Learning (DL) has shown real promise for the classification efficiency for emotion recognition problems. In this paper we present experimental results for a deeply-trained model for emotion recognition through the use of facial expression images. We explore two Convolutional Neural Network (CNN) architectures that offer automatic feature extraction and representation, followed by fully connected softmax layers to classify images into seven emotions. The first architecture explores the impact of reducing the number of deep learning layers and the second splits the input images horizontally into two streams based on eye and mouth positions. The first proposed architecture produces state of the art results with an accuracy rate of 96.93 % and the second architecture with split input produces an average accuracy rate of 86.73 %, respectively.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2016
Editors	Alessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
Place of Publication	Switzerland
Publisher	Springer Verlag
Pages	38-46
Volume	9887
ISBN (Print)	978-3-319-44780-3, 978-3-319-44781-0
DOIs	https://doi.org/10.1007/978-3-319-44781-0_5
Publication status	Published - 13 Aug 2016
Event	The 25th International Conference on Artificial Neural Networks - Barcelona, Spain Duration: 6 Sept 2016 → 9 Sept 2016

Conference

Conference	The 25th International Conference on Artificial Neural Networks
Abbreviated title	ICANN 2016
Country/Territory	Spain
City	Barcelona
Period	6/09/16 → 9/09/16

Bibliographical note

The full text is not available on the repository.

Keywords

Deep learning
Convolution neural networks
Emotion recognition
Empathic robots

Access to Document

10.1007/978-3-319-44781-0_5

Cite this

Deep Learning for Emotion Recognition in Faces. / Ruiz-Garcia, Ariel; Elshaw, Mark; Altahhan, Abdulrahman et al.
Artificial Neural Networks and Machine Learning – ICANN 2016. ed. / Alessandro E.P. Villa; Paolo Masulli; Antonio Javier Pons Rivero. Vol. 9887 Switzerland: Springer Verlag, 2016. p. 38-46.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Ruiz-Garcia, A, Elshaw, M, Altahhan, A & Palade, V 2016, Deep Learning for Emotion Recognition in Faces. in AEP Villa, P Masulli & AJP Rivero (eds), Artificial Neural Networks and Machine Learning – ICANN 2016. vol. 9887, Springer Verlag, Switzerland, pp. 38-46, The 25th International Conference on Artificial Neural Networks, Barcelona, Spain, 6/09/16. https://doi.org/10.1007/978-3-319-44781-0_5

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AB - Deep Learning (DL) has shown real promise for the classification efficiency for emotion recognition problems. In this paper we present experimental results for a deeply-trained model for emotion recognition through the use of facial expression images. We explore two Convolutional Neural Network (CNN) architectures that offer automatic feature extraction and representation, followed by fully connected softmax layers to classify images into seven emotions. The first architecture explores the impact of reducing the number of deep learning layers and the second splits the input images horizontally into two streams based on eye and mouth positions. The first proposed architecture produces state of the art results with an accuracy rate of 96.93 % and the second architecture with split input produces an average accuracy rate of 86.73 %, respectively.

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ER -

Deep Learning for Emotion Recognition in Faces

Abstract

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Bibliographical note

Keywords

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