Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Hybrid Representation of Word Images for Keyword Spotting Read chapter Read first chapter

2020 | OriginalPaper | Chapter

EMOTIONCAPS - Facial Emotion Recognition Using Capsules

Authors : Bhavya Shah, Krutarth Bhatt, Srimanta Mandal, Suman K. Mitra

Published in: Neural Information Processing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Facial emotion recognition plays an important role in day-to-day activities. To address this, we propose a novel encoder/decoder network namely EmotionCaps, which models the facial images using matrix capsules, where hierarchical pose relationships between facial parts are built into internal representations. An optimal number of capsules and their dimension is chosen, as these hyper-parameters in the network play an important role to capture the complex facial pose relationship. Further, the batch normalization layer is introduced to expedite the convergence. To show the effectiveness of our network, EmotionCaps is evaluated for seven basic emotions in a wide range of head orientations. Additionally, our method is able to analyze facial images even in the presence of noise and blur quite accurately.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Efficient Segmentation Pyramid Network
next chapter End-to-end Saliency-Guided Deep Image Retrieval
Literature
1.
Abien Fred, M.A.: Deep learning using rectified linear units (ReLU). Neural Evol. Comput. 1, 7 p. (2018)
2.
Arriaga, O., Valdenegro, M., Plöger, P.: Real-time convolutional neural networks for emotion and gender classification. In: ESANN, pp. 221–226 (2019)
3.
Carrier, P.L., Courville, A., Goodfellow, I.J., Mirza, M., Bengio, Y.: Fer-2013 face database. Technical report (2013)
4.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)
5.
Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recogn. 36(1), 259–275 (2003)CrossRef
6.
Hosseini, S., Cho, N.I.: Gf-CapsNet: Using Gabor jet and capsule networks for facial age, gender, and expression recognition. In: FG, pp. 1–8 (2019)
7.
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, ICML 2015, vol. 37, pp. 448–456. JMLR.org (2015)
8.
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)
9.
Ko, B.C.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), 401 (2018)CrossRef
10.
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE TIP 11(4), 467–476 (2002)
11.
Liu, P., Han, S., Meng, Z., Tong, Y.: Facial expression recognition via a boosted deep belief network. In: CVPR, pp. 1805–1812 (2014)
12.
Lopes, A.T., de Aguiar, E., Souza, A.F.D., Oliveira-Santos, T.: Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recogn. 61, 610–628 (2017)CrossRef
13.
Marrero Fernandez, P.D., Guerrero Pena, F.A., Ing Ren, T., Cunha, A.: FERAtt: facial expression recognition with attention net. In: CVPR Workshops, pp. 1–10 (2019)
14.
Minaee, S., Abdolrashidi, A.: Deep-emotion: facial expression recognition using attentional convolutional network. CoRR abs/1902.01019 (2019)
15.
Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: WACV, pp. 1–10 (2016)
16.
Pramerdorfer, C., Kampel, M.: Facial expression recognition using convolutional neural networks:state of the art. CoRR abs/1612.02903 (2016)
17.
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: NIPS, pp. 3856–3866 (2017)
18.
Tang, Y.: Deep learning using linear support vector machines. In: ICML, pp. 1–6 (2013)
19.
20.
Viola, P., Jones, M., et al.: Rapid object detection using a boosted cascade of simple features. In: CVPR(1), vol. 1, no. 511–518, p. 3 (2001)
21.
Yu, Z., Zhang, C.: Image based static facial expression recognition with multiple deep network learning. In: ICMI, ICMI 2015, pp. 435–442. ACM, New York (2015)
22.
Zeng, N., Zhang, H., Song, B., Liu, W., Li, Y., Dobaie, A.M.: Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273, 643–649 (2018)CrossRef
23.
Zhang, F., Zhang, T., Mao, Q., Xu, C.: Joint pose and expression modeling for facial expression recognition. In: CVPR, pp. 3359–3368 (2018)
24.
Zhang, T., Zheng, W., Cui, Z., Zong, Y., Yan, J., Yan, K.: A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans. Multimed. 18(12), 2528–2536 (2016)CrossRef
Metadata
Title
EMOTIONCAPS - Facial Emotion Recognition Using Capsules
Authors
Bhavya Shah
Krutarth Bhatt
Srimanta Mandal
Suman K. Mitra
Copyright Year
2020
Book
Neural Information Processing

Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63820-7_45

Premium Partner

    Image Credits