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Cited by 17Emotion and Mood RecognitionFace recognition and analysisFace and Expression Recognition

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Improving Facial Emotion Recognition Systems with Crucial Feature Extractors

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Pandey, Ram Krishna, Karmakar, Souvik, Ramakrishnan, A. G., Saha, N.·2019·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-30642-7_24
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Research works printed but not linked: 31

  1. El Ayadi, M., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recogn. 44(3), 572–587 (2011) · DOI
  2. Davidson, R.J., Ekman, P., Saron, C.D., Senulis, J.A., Friesen, W.V.: Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology: I. J. Pers. Soc. Psychol. 58(2), 330 (1990) · DOI
  3. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1982) · DOI · Paper page
  4. Greenwood, D.D.: A cochlear frequencyposition function for several species-29 years later. J. Acoust. Soc. Am. 87(6), 2592–2605 (1990) · DOI
  5. Darwin, C., Prodger, P.: The Expression of the Emotions in Man and Animals. Oxford University Press, Oxford (1998) · DOI
  6. Ekman, P., Friesen, W.V.: Nonverbal leakage and clues to deception. Psychiatry 32(1), 88–106 (1969) · DOI · Paper page
  7. Ekman, P., Dacher, K.: Universal facial expressions of emotion. Calif. Ment. Health Res. Digest 8(4), 151–158 (1970)
  8. Friesen, E., Ekman, P.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)
  9. Suk, M., Prabhakaran, B.: Real-time mobile facial expression recognition system-a case study. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014) · DOI
  10. Ghimire, D., Lee, J.: Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines. Sensors 13(6), 7714–7734 (2013) · DOI
  11. Happy, S.L., George, A., Routray, A.: A real time facial expression classification system using local binary patterns. In: 4th International Conference on Intelligent Human Computer Interaction (IHCI), pp. 1–5. IEEE (2012) · DOI
  12. Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007) · DOI · Paper page
  13. Ghimire, D., Jeong, S., Lee, J., Park, S.H.: Facial expression recognition based on local region specific features and support vector machines. Multimed. Tools Appl. 76(6), 7803–7821 (2017) · DOI
  14. Bai, Y., Guo, L., Jin, L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: International Conference on Image processing (ICIP) (2009)
  15. Breuer, R., Kimmel, R.: A deep learning perspective on the origin of facial expressions. arXiv preprint arXiv:1705.01842 (2017) · arXiv
  16. Chollet, F.: Xception: deep learning with separable convolutions. arXiv Preprint arXiv1610 2357 (2016) · DOI · Paper page
  17. Arriaga, O., Valdenegro-Toro, M., Ploger, P.: Real-time convolutional neural networks for emotion and gender classification. arXiv preprint arXiv:1710.07557 (2017) · arXiv
  18. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) · arXiv
  19. Ouellet, S.: Real-time emotion recognition for gaming using deep convolutional network features. arXiv preprint arXiv:1408.3750 (2014) · arXiv
  20. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended cohn-kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Workshops, San Francisco, CA (2010) · DOI · Paper page
  21. Lundqvist, D., Flykt, A., Öhman, A.: The Karolinska directed emotional faces (KDEF). CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet, pp. 91–630 (1998) · DOI
  22. Barsoum, E., Zhang, C., Ferrer, C.C., Zhang, Z.: Training deep networks for facial expression recognition with crowd-sourced label distribution. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction. ACM (2016) · DOI · Paper page
  23. Jaderberg, M., Simonyan, K., Zisserman, A.: Spatial transformer networks. In: Advances in Neural Information Processing Systems (2015)
  24. Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. Addison-Wesley, Boston (1992)
  25. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013) · arXiv
  26. Kingma, D.P., Ba, L.J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
  27. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018) · DOI · Paper page
  28. Schomaker, L.: Anticipation in cybernetic systems: a case against mindless anti-representationalism. In: International Conference on Systems, Man and Cybernetics, vol. 2. IEEE (2004)
  29. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014) · arXiv
  30. Dhall, A., Ramana Murthy, O.V., Goecke, R., Joshi, J., Gedeon, T.: Video and image based emotion recognition challenges in the wild: EmotiW 2015. In: Proceedings International Conference on Multimodal Interaction, pp. 423–426. ACM (2015) · DOI · Paper page
  31. Krishna Pandey, R., Karmakar, S., Ramakrishnan, A.G., Saha, N.: Improving facial emotion recognition systems using gradient and Laplacian images. arXiv preprint arXiv:1902.05411 (2019) · arXiv

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Metadata

Authors
Pandey, R. K., Karmakar, S., Ramakrishnan, A. G., & Saha, N.
Affiliations
Indian Institute of Science Bangalore (IN)
Corresponding author
Pandey, Ram Krishna
ORCID
Ramakrishnan, A. G.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2019
Pages
268-279
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-30642-7_24
OpenAlex
W2971532285
Cited by
17
References
2
Citations by year
2026: 2 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 2 · 2020: 1 · 2019: 2
FWCI
2.52
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords
facial emotion recognition, fiducial points, gradient features, spatial transformer network, lightweight CNN architecture, feature fusion, KDEF dataset

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