Facial emotion recognition based on AlexNet with large margin cosine loss function

Yan Zhang; Yaping Dai; Lei Wang; Zhiyang Jia; Kaoru Hirota

Facial emotion recognition based on AlexNet with large margin cosine loss function

Yan Zhang, Yaping Dai, Lei Wang, Zhiyang Jia^*, Kaoru Hirota

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

In order to improve the accuracy of facial emotion recognition, a Large Margin Cosine Loss(LMCL) function is proposed combined with convolutional neural network (CNN) for emotion recognition. The LMCL function is a loss function which could increase the differences of features among samples from different classes(inter-class), meanwhile reduce the differences of features among samples from same classes (inner-class). A reliable and simple neural network Alexnet is customized and trained for this task with dataset FER-2013, a wildly used dataset for emotion recognition. Compared with the neural network with original loss function cross entropy with softmax(The following is referred to as the following softmax loss function), the accuracy of neural network model is improved by 21.57% with LMCL function in experiment. As well as the error rate of different classes recognition is reduced.

Original language	English
Publication status	Published - 2018
Event	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 - Tengzhou, Shandong, China Duration: 2 Nov 2018 → 6 Nov 2018

Conference

Conference	8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018
Country/Territory	China
City	Tengzhou, Shandong
Period	2/11/18 → 6/11/18

Keywords

Alexnet
Emotion Recognition
Human Machine Interaction
Image Processing
LMCL Function

Cite this

Zhang, Y., Dai, Y., Wang, L., Jia, Z., & Hirota, K. (2018). Facial emotion recognition based on AlexNet with large margin cosine loss function. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Zhang, Yan ; Dai, Yaping ; Wang, Lei et al. / Facial emotion recognition based on AlexNet with large margin cosine loss function. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

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title = "Facial emotion recognition based on AlexNet with large margin cosine loss function",

abstract = "In order to improve the accuracy of facial emotion recognition, a Large Margin Cosine Loss(LMCL) function is proposed combined with convolutional neural network (CNN) for emotion recognition. The LMCL function is a loss function which could increase the differences of features among samples from different classes(inter-class), meanwhile reduce the differences of features among samples from same classes (inner-class). A reliable and simple neural network Alexnet is customized and trained for this task with dataset FER-2013, a wildly used dataset for emotion recognition. Compared with the neural network with original loss function cross entropy with softmax(The following is referred to as the following softmax loss function), the accuracy of neural network model is improved by 21.57% with LMCL function in experiment. As well as the error rate of different classes recognition is reduced.",

keywords = "Alexnet, Emotion Recognition, Human Machine Interaction, Image Processing, LMCL Function",

author = "Yan Zhang and Yaping Dai and Lei Wang and Zhiyang Jia and Kaoru Hirota",

note = "Publisher Copyright: {\textcopyright} ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.; 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018 ; Conference date: 02-11-2018 Through 06-11-2018",

year = "2018",

language = "English",

}

Zhang, Y, Dai, Y, Wang, L, Jia, Z & Hirota, K 2018, 'Facial emotion recognition based on AlexNet with large margin cosine loss function', Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China, 2/11/18 - 6/11/18.

Facial emotion recognition based on AlexNet with large margin cosine loss function. / Zhang, Yan; Dai, Yaping; Wang, Lei et al.
2018. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Facial emotion recognition based on AlexNet with large margin cosine loss function

AU - Zhang, Yan

AU - Dai, Yaping

AU - Wang, Lei

AU - Jia, Zhiyang

AU - Hirota, Kaoru

N1 - Publisher Copyright: © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Application.

PY - 2018

Y1 - 2018

N2 - In order to improve the accuracy of facial emotion recognition, a Large Margin Cosine Loss(LMCL) function is proposed combined with convolutional neural network (CNN) for emotion recognition. The LMCL function is a loss function which could increase the differences of features among samples from different classes(inter-class), meanwhile reduce the differences of features among samples from same classes (inner-class). A reliable and simple neural network Alexnet is customized and trained for this task with dataset FER-2013, a wildly used dataset for emotion recognition. Compared with the neural network with original loss function cross entropy with softmax(The following is referred to as the following softmax loss function), the accuracy of neural network model is improved by 21.57% with LMCL function in experiment. As well as the error rate of different classes recognition is reduced.

AB - In order to improve the accuracy of facial emotion recognition, a Large Margin Cosine Loss(LMCL) function is proposed combined with convolutional neural network (CNN) for emotion recognition. The LMCL function is a loss function which could increase the differences of features among samples from different classes(inter-class), meanwhile reduce the differences of features among samples from same classes (inner-class). A reliable and simple neural network Alexnet is customized and trained for this task with dataset FER-2013, a wildly used dataset for emotion recognition. Compared with the neural network with original loss function cross entropy with softmax(The following is referred to as the following softmax loss function), the accuracy of neural network model is improved by 21.57% with LMCL function in experiment. As well as the error rate of different classes recognition is reduced.

KW - Alexnet

KW - Emotion Recognition

KW - Human Machine Interaction

KW - Image Processing

KW - LMCL Function

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M3 - Paper

AN - SCOPUS:85057992670

T2 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018

Y2 - 2 November 2018 through 6 November 2018

ER -

Zhang Y, Dai Y, Wang L, Jia Z , Hirota K. Facial emotion recognition based on AlexNet with large margin cosine loss function. 2018. Paper presented at 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and Control Applications, ISCIIA and ITCA 2018, Tengzhou, Shandong, China.

Facial emotion recognition based on AlexNet with large margin cosine loss function

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