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A Survey on Facial Expression Recognition Techniques

Tejaswi Satepuri1 , P. Chandrasekar Reddy2

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 980-984, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.980984

Online published on May 31, 2019

Copyright © Tejaswi Satepuri, P. Chandrasekar Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Tejaswi Satepuri, P. Chandrasekar Reddy, “A Survey on Facial Expression Recognition Techniques,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.980-984, 2019.

MLA Style Citation: Tejaswi Satepuri, P. Chandrasekar Reddy "A Survey on Facial Expression Recognition Techniques." International Journal of Computer Sciences and Engineering 7.5 (2019): 980-984.

APA Style Citation: Tejaswi Satepuri, P. Chandrasekar Reddy, (2019). A Survey on Facial Expression Recognition Techniques. International Journal of Computer Sciences and Engineering, 7(5), 980-984.

BibTex Style Citation:
@article{Satepuri_2019,
author = {Tejaswi Satepuri, P. Chandrasekar Reddy},
title = {A Survey on Facial Expression Recognition Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {980-984},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4349},
doi = {https://doi.org/10.26438/ijcse/v7i5.980984}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.980984}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4349
TI - A Survey on Facial Expression Recognition Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - Tejaswi Satepuri, P. Chandrasekar Reddy
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 980-984
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

Facial image analysis is a important and mainstream research point and it incorporates face detection, face recognition, facial expression analysis, and a few other related applications. LBP is a non-parametric descriptor whose point is to proficiently condense the neighborhood structures of images. As of late, it has stirred expanding enthusiasm for some territories of image processing and computer vision, and has demonstrated its viability in various applications, specifically for facial image analysis, including undertakings as assorted as face detection, face recognition, facial expression analysis, statistic classification, and so on. This paper presents a comprehensive overview of Gabor Filter and SVM, Genetic Algorithms and Neural Network and at long last CNN including a few later variations. LBP-based facial image analysis is widely checked on, while its fruitful expansions in managing different errands of facial image analysis are likewise featured.

Key-Words / Index Term

Facial expressions recognition, LBP, human cognition, emotion model, machine learning

References

[1] Mehrabian, A. Communication without words. Psychol. Today 1968, 2, 53–56.
[2] Kaulard, K.; Cunningham, D.W.; Bülthoff, H.H.; Wallraven, C. The MPI facial expression Database—A validated database of emotional and conversational facial expressions. PLoS ONE 2012, 7, e32321.
[3] Dornaika, F.; Raducanu, B. Efficient facial expression recognition for human robot interaction. In Proceedings of the 9th InternationalWork-Conference on Artificial Neural Networks on Computational and Ambient Intelligence, San Sebastián, Spain, 20–22 June 2007; pp. 700–708.
[4] Bartneck, C.; Lyons, M.J. HCI and the face: Towards an art of the soluble. In Proceedings of the International Conference on Human-Computer Interaction: Interaction Design and Usability, Beijing, China, 22–27 July 2007; pp. 20–29.
[5] Hickson, S.; Dufour, N.; Sud, A.; Kwatra, V.; Essa, I.A. Eyemotion: Classifying facial expressions in VR using eye-tracking cameras. arXiv 2017, arxiv:1707.07204.
[6] Chen, C.H.; Lee, I.J.; Lin, L.Y. Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Res. Dev. Disabil. 2015, 36, 396–403.
[7] Assari, M.A.; Rahmati, M. Driver drowsiness detection using face expression recognition. In Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia, 16–18 November 2011; pp. 337–341.
[8] Zhan, C.; Li,W.; Ogunbona, P.; Safaei, F. A real-time facial expression recognition system for online games. Int. J. Comput. Games Technol. 2008, 2008.
[9] Mourão, A.; Magalhães, J. Competitive affective gaming: Winning with a smile. In Proceedings of the ACM International Conference on Multimedia, Barcelona, Spain, 21–25 October 2013; pp. 83–92.
[10] 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 Proceedings of the IEEE Conference on Computer Vision and Pattern RecognitionWorkshops, San Francisco, CA, USA, 13–18 June 2010; pp. 94–101.
[11] Kahou, S.E.; Michalski, V.; Konda, K. Recurrent neural networks for emotion recognition in video. In Proceedings of the ACM on International Conference on Multimodal Interaction, Seattle, WA, USA, 9–13 November 2015; pp. 467–474.
[12] Walecki, R.; Rudovic, O. Deep structured learning for facial expression intensity estimation. Image Vis. Comput. 2017, 259, 143–154.
[13] Kim, D.H.; Baddar,W.; Jang, J.; Ro, Y.M.Multi-objective based Spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Affect. Comput. 2017, PP.
[14] Ekman, P.; Friesen,W.V. Facial Action Coding System: Investigator’s Guide, 1st ed.; Consulting Psychologists Press: Palo Alto, CA, USA, 1978; pp. 1–15, ISBN 9993626619.
[15] Hamm, J.; Kohler, C.G.; Gur, R.C.; Verma, R. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J. Neurosci. Methods 2011, 200, 237–256.
[16] Jeong, M.; Kwak, S.Y.; Ko, B.C.; Nam, J.Y. Driver facial landmark detection in real driving situation. IEEE Trans. Circuits Syst. Video Technol. 2017, 99, 1–15.
[17] Tao, S.Y.; Martinez, A.M. Compound facial expressions of emotion. Natl. Acad. Sci. 2014, 111, E1454–E1462.
[18] Benitez-Quiroz, C.F.; Srinivasan, R.; Martinez, A.M. EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 5562–5570.
[19] Kolakowaska, A. A review of emotion recognition methods based on keystroke dynamics and mouse movements. In Proceedings of the 6th International Conference on Human System Interaction, Gdansk, Poland, 6–8 June 2013; pp. 548–555.