An Optimal approach of Emotion Detection Using Deep Learning

Authors

  • Priti Singh Department of Computer Science & Engineering, FET, Rama University, Kanpur 209217, INDIA
  • Hari Om Sharan Department of Computer Science & Engineering, FET, Rama University, Kanpur 209217, INDIA
  • C. S. Raghuvanshi Department of Computer Science & Engineering, FET, Rama University, Kanpur 209217, INDIA

Keywords:

Deep learning, facial emotion recognition, Cognitive Internet of Things

Abstract

Smart facial emotion detection is a fascinating field of research that has been presented and implemented in several fields, including defense, health, and human-machine interfaces. Researchers in this area are focusing on strategies to encrypt, decode and erase facial expressions to enhance algorithm prediction. Various deep learning algorithms and cognitive internet of thing (CIoT) are being used to improve efficiency due to the exponential development of this technology. The aim of this work is to provide a summary of recent work on smart facial expression recognition using deep learning Algorithm and define new approach of emotion detection. Due to the exponential growth of the Internet of Things, current internet of thing-based technologies around automated intelligent services lacks technological resources, meaning that they would be unable to meet the needs of industrial services. The incremental enrichment of internet of thing technology for smart environments has resulted in technology delays and decreased market productivity. Deep learning is one of the most used machine learning techniques in a variety of applications and experiments. Designing an emotional intelligent approach and deep learning that will inspire internet of thing is a pressing need to solve this problem, according to Recent Application in facial Emotion detection.

Downloads

Download data is not yet available.

References

M. Chen, F. Herrera, K. Hwang,"Cognitive computing: architecture, technologies and intelli-gent applications", IEEE Access, vol 6, pp. 19774–19783, Jan 2018.

M. Alhussein, G. Muhammad, M.S. Hossain, S.U. Amin, "Cognitive IoT-cloud integra-tion for smart healthcare: case study for epileptic seizure detection and monitoring", Mob NetwAppl, vol. 23, pp. 1624–1635, Sep 2018.

S. Gupta, A.K. Kar, A. Baabdullah, A.A. Wassan, Al. Khowaiter, "Big data with cognitive computing: a review for the future", International Journal of Information Management, vol. 69, pp. 78–89, Oct 2018.

H. Xu, W. Yu, D. Grifth, N. Golmie., "A survey on industrial internet of things: a cyber-physical systems perspective", IEEE Access, vol. 6, pp. 78238–78259, Dec. 2018

A. Sheth, "Internet of things to smart IoT through semantic, cognitive, and perceptual com-puting", IEEE Intelligent Systems, vol. 31(2), pp. 108–112, Mar.-Apr. 2016.

P. Vlacheas, R. Giafreda, V. Stavroulaki, D. Kelaidonis, V. Foteinos, G. Poulios, P. De-mestichas, A. Somov, A.R. Biswas, K. Moessner, "Enabling smart cities through a cognitive management framework for the internet of things", IEEE Communications Magazine, vol. 51, pp. 102–111, June 2013.

Ekman, Paul, and Wallace V. Friesen. ”Constants across cultures in the face and emotion.” Journal of personality and social psychology 17.2: 124, 1971.

X. P. Burgos-Artizzu, P. Perona, and P. Dollar. Robust face landmark ´ estimation under occlusion. In Proc. Int. Conf. Comput. Vision, pages 1513–1520. IEEE, 2013.

D. E. King. Dlib-ml: A machine learning toolkit. J. Mach. Learning Research, 10(Jul):1755–1758, 2009.

Y. Wu, T. Hassner, K. Kim, G. Medioni, and P. Natarajan. Facial landmark detection with tweaked convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.

T. Baltrusaitis, P. Robinson, and L.-P. Morency. Openface: an open ˇ source facial behavior analysis toolkit. In Winter Conf. on App. of Comput. Vision, 2016.

A. Zadeh, T. Baltrusaitis, and L.-P. Morency. Convolutional experts ˇ constrained local model for facial landmark detection. In Proc. Conf. Comput. Vision Pattern Recognition Workshops, 2017.

X. Zhu, Z. Lei, X. Liu, H. Shi, and S. Li. Face alignment across large poses: A 3D solution. In Proc. Conf. Comput. Vision Pattern Recognition, Las Vegas, NV, June 2016.

Hough, Paul VC. ”Method and means for recognizing complex patterns.” U.S. Patent 3,069,654, issued December 18, 1962.

Shan, Caifeng, Shaogang Gong, and Peter W. McOwan. ”Facial expression recognition based on local binary patterns: A comprehensive study.” Image and vision Computing 27.6: 803-816, 2009.

Chen, Junkai, Zenghai Chen, Zheru Chi, and Hong Fu. ”Facial expression recognition based on facial components detection and hog features.” In International workshops on electrical and computer engineering subfields, pp. 884-888, 2014.

Whitehill, Jacob, and Christian W. Omlin. ”Haar features for facs au recognition.” In Auto-matic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on, pp. 5-pp. IEEE, 2006.

Edwards, Jane, Henry J. Jackson, and Philippa E. Pattison. ”Emotion recognition via facial expression and affective prosody in schizophrenia: a methodological review.” Clinical psy-chology review 22.6: 789-832, 2002.

C. Fabian Benitez-Quiroz, R. Srinivasan, and A. M. Martinez. Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In Proc. Conf. Comput. Vision Pattern Recognition, pages 5562–5570, 2016

S. Zafeiriou, A. Papaioannou, I. Kotsia, M. Nicolaou, and G. Zhao. Facial affect“in-the-wild”. In Proc. Conf. Comput. Vision Pattern Recognition Workshops, pages 36–47, 2016.

R. Kosti, J. M. Alvarez, A. Recasens, and A. Lapedriza. Emotion recognition in context. In Proc. Conf. Comput. Vision Pattern Recognition, 2017.

G. Levi and T. Hassner. Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In Int. Conf. on Multimodal Interaction, pages 503–510. ACM, 2015.

K. Zhang, L. Tan, Z. Li, and Y. Qiao. Gender and smile classification using deep convolu-tional neural networks. In Proc. Conf. Comput. Vision Pattern Recognition Workshops, pages 34–38, 2016.

M. Naphade, J. Smith, J. Tesic, S.-F. Chang, W. Hsu, L. Kennedy, A. Hauptmann, and Curtis J. Large-scale concept ontology for multimedia. In IEEE Multimedia, 2006.

J.R. Smith, M. Naphade, and A. Natsev. Multimedia semantic indexing using model vec-tors. In International Conference on Multimedia and Expo, 2003

Genevieve Patterson and James Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In Computer Vision and Pattern Recognition. IEEE, 2012.

Yanwei Fu, Timothy M Hospedales, Tao Xiang, and Shaogang Gong. Attribute learning for understanding unstructured social activity. In European Conference on Computer Vision. Springer, 2012.

Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y Ng. Self-taught learning: transfer learning from unlabeled data. In Proceedings of the 24th international confer-ence on Machine learning, pages 759–766. ACM, 2007.

GrégoireMesnil, Yann Dauphin, Xavier Glorot, Salah Rifai, YoshuaBengio, Ian J Goodfel-low, Erick Lavoie, Xavier Muller, Guillaume Desjardins, David Warde-Farley, et al. Unsuper-vised and transfer learning challenge: a deep learning approach. In ICML Unsupervised and Transfer Learning, pages 97–110, 2012.

Lyndon Kennedy and Alexander Hauptmann. Lscom lexicon definitions and annotations (version 1.0). 2006.

F. Khan(2018 Dec. 10), “Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks”(Online), Department of Electronics and Commu-nication Engineering, NIT Karnataka, Mangalore, India, IJACSA, Available: https://www.groundai.com/project/facial-expression-recognition-using-facial-landmark-detection-and-feature-extraction-on-neural- networks/

S. Mishra, G.R.B. Prasada, R.K. Kumar, G. Sanyal (2018 Dec. 10), “Emotion Recognition Through Facial Gestures — A Deep Learning Approach”, Mining Intelligence and Knowledge Exploration” (Online), Available: https://link.springer.com/chapter/10.1007/978-3-319-71928-3_2

R. Walecki, O. Rudovic, V.Pavlovic, B. Schuller, M. Pantic (2018 Dec. 10), “Deep Struc-tured Learning for Facial Action Unit Intensity Estimation”, IJACSA (Online), Available: https://ibug.doc.ic.ac.uk/media/uploads/documents/deep-structured-learning.pdf

Downloads

Published

24.03.2024

How to Cite

Singh, P. ., Sharan, H. O. ., & Raghuvanshi, C. S. . (2024). An Optimal approach of Emotion Detection Using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 477–482. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4992

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.