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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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Towards Real-Life Facial Expression Recognition Systems

BENTA, K.-I. See more information about BENTA, K.-I. on SCOPUS See more information about BENTA, K.-I. on IEEExplore See more information about BENTA, K.-I. on Web of Science, VAIDA, M.-F. See more information about VAIDA, M.-F. on SCOPUS See more information about VAIDA, M.-F. on SCOPUS See more information about VAIDA, M.-F. on Web of Science
 
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Download PDF pdficon (1,034 KB) | Citation | Downloads: 345 | Views: 1,984

Author keywords
facial expression recognition, affective computing, feature extraction, classification, database

References keywords
recognition(74), facial(70), computing(20), pattern(19), emotion(16), analysis(15), automatic(14), affective(14), image(12), zhang(10)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 93 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02012
Web of Science Accession Number: 000356808900012
SCOPUS ID: 84979726417

Abstract
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Facial expressions are a set of symbols of great importance for human-to-human communication. Spontaneous in their nature, diverse and personal, facial expressions demand for real-time, complex, robust and adaptable facial expression recognition (FER) systems to facilitate the human-computer interaction. The last years' research efforts in the recognition of facial expressions are preparing FER systems to step into the real-life. In order to meet the before-mentioned requirements, this article surveys the work in FER since 2008, particularly adopting the discrete states emotion model in a quest for the most valuable FER works/systems. We first present the new spontaneous facial expression databases and then organize the real-time FER solutions grouped by spontaneous and posed facial expression databases. Then automatic FERs are compared and the cross-database validation method is presented. Finally, we outline FER system open issues to meet real-life challenges.


References | Cited By  «-- Click to see who has cited this paper

[1] J. F. Grafsgaard, J. B. Wiggins, K. E. Boyer, E. N. Wiebe, J. C. Lester, "Automatically recognizing facial expression: predicting engagement and frustration," Proceedings of the 6th International Conference on Educational Data Mining, 2013.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]


[2] C. N. Moridis, A. A. Economides, "Affective Learning: Empathetic Agents with Emotional Facial and Tone of Voice Expressions," IEEE Transactions on Affective Computing, vol. 3, no. 3, pp. 260-272, July-Sept. 2012.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 21]


[3] M. (E.) Hoque, M. Courgeon, J.-C. Martin, B. Mutlu, R. W. Picard, "MACH: My automated conversation coacH," Proceedings of the 2013 ACM International Joint Conference on Pervasive and ubiquitous computing. ACM, pp. 697-706, 2013.
[CrossRef] [SCOPUS Times Cited 33]


[4] S. J. Ahn, J. Bailenson, J. Fox, M. Jabon, "Using automated facial expression analysis for emotion and behavior prediction," The Routledge Handbook of Emotions and Mass Media, pp. 349, 2010. Available: http://vhil.stanford.edu/pubs/2010/ahn-hemm-facial-expression.pdf

[5] H.-J. Kim, Y. S. Choi, "EmoSens: Afective entity scoring, a novel service recommendation framework for mobile platform," Workshop on personalization in mobile application of the 5th international conference on recommender system, 2011. Available: http://pema2011.cs.ucl.ac.uk/papers/pema2011_kim.pdf

[6] A. Kolakowska, A. Landowska, M. Szwoch, W. Szwoch, M.R. Wróbel, "Emotion Recognition and Its Applications," In Human-Computer Systems Interaction: Backgrounds and Applications 3, pp. 51-62, Springer International Publishing, 2014.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 3]


[7] K.-I. Benta, M. Cremene, V., Todica, "Towards an affective aware home," In Ambient Assistive Health and Wellness Management in the Heart of the City, pp. 74-81, Springer Berlin Heidelberg, 2009.
[CrossRef] [SCOPUS Times Cited 3]


[8] G. Castellano, H. Gunes, C. Peters, B. Schuller, "Multimodal Affect Recognition for Naturalistic Human-Computer and Human-Robot Interactions", invited chapter for Handbook of Affective Computing, R. A. Calvo, S. D'Mello, J. Gratch, A. Kappas (eds.), Oxford University Press, pp. 246-257, 2015.
[CrossRef]


[9] P. Marrero-Fernandez, A. Montoya-Padrón, A. Jaume-i-Capo, J.M. Buades Rubio, "Evaluating the Research in Automatic Emotion Recognition," IETE Technical Review, vol. 31, no. 3, 220-232, 2014.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[10] G. Littlewort, J. Whitehill, T. Wu, I. Fasel, M. Frank, J. Movellan, M. Bartlett, "The computer expression recognition toolbox (CERT)," Automatic Face & Gesture Recognition and Workshops (FG 2011), IEEE International Conference, 2011.
[CrossRef] [SCOPUS Times Cited 128]


[11] M. Pantic, L. J. M. Rothkrantz, "Automatic analysis of facial expressions: The state of the art," IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424-1445, on december, 2000.
[CrossRef] [Web of Science Times Cited 719] [SCOPUS Times Cited 986]


[12] M. Pantic, L. J. M. Rothkrantz, "Toward an affect-sensitive multimodal human-computer interaction," Proceedings of the IEEE 91.9, pp. 1370-1390, 2003.
[CrossRef] [Web of Science Times Cited 318] [SCOPUS Times Cited 438]


[13] B. Fasel, J. Luettin, "Automatic facial expression analysis: a survey," Pattern Recognition, Volume 36, Issue 1, January 2003, Pages 259-275.
[CrossRef] [Web of Science Times Cited 717] [SCOPUS Times Cited 995]


[14] Z. Zeng, M. Pantic, G. I. Roisman, T. S. Huang, "A survey of affect recognition methods: audio, visual, and spontaneous expressions," IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 31, no.1, pp. 39-58, 2009.
[CrossRef] [Web of Science Times Cited 781] [SCOPUS Times Cited 1108]


[15] C. H. Wu, J. C. Lin, W.L. Wei, "Survey on audiovisual emotion recognition: databases, features, and data fusion strategies," APSIPA Transactions on Signal and Information Processing, vol. 3, e12, 2014.
[CrossRef] [SCOPUS Times Cited 10]


[16] J. Cohn, F. De La Torre, "Automated Face Analysis for Affective Computing". 2015. Handbook of Affective Computing, R. A. Calvo, S. D'Mello, J. Gratch, A. Kappas (eds.), pp. 131-150, Oxford University Press.
[CrossRef]


[17] E. Sariyanidi, H. Gunes, A. Cavallaro, "Automatic analysis of facial affect: A survey of registration, representation and recognition," IEEE Transactions on Pattern Analysis & Machine Intelligence, no. 1, pp. 1, 2014.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 36]


[18] R. W. Picard, "Emotion research by the people, for the people," Emotion Rev., vol. 2, pp. 250-254, 2010.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 49]


[19] D. A. G. Jauregui, J.-C. Martin, "Evaluation of Vision-based Real-Time Measures for Emotions Discrimination under Uncontrolled Conditions," Proceeding EmotiW '13 Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop, pp. 17-22, 2013.
[CrossRef] [SCOPUS Record]


[20] R. A. Calvo, S. D'Mello, "Affect detection: an interdisciplinary review of models, methods, and their applications," IEEE Trans. On Affective Computing, vol. 1, no. 1, pp. 18-37, 2010.
[CrossRef] [Web of Science Times Cited 303] [SCOPUS Times Cited 466]


[21] H. Gunes, M. Pantic, "Automatic, dimensional and continuous emotion recognition," International Journal of Synthetic Emotions, vol. 1, is. 1, pp. 68-99, 2010.
[CrossRef]


[22] H. Gunes, B. Schuller, M. Pantic, R. Cowie, "Emotion representation, analysis and synthesis in continuous space: a survey," Automatic Face & Gesture Recognition and Workshops (FG 2011), pp. 827-834, IEEE International Conference on 21-25 March, 2011.
[CrossRef] [SCOPUS Times Cited 77]


[23] K.-I. Benta, H.-I. Lisei, M. Cremene, "Towards a Unified 3D Affective Model," Doctoral Consortium Proceedings of International Conference on Affective Computing and Intelligent Interaction (ACII2007), Lisbon, Portugal, 12-14 September 2007, pp. 75-85. Available: www.di.uniba.it/intint/DC-ACII07/Benta.pdf

[24] H. Gunes, B. Schuller, "Categorical and dimensional affect analysis in continuous input: Current trends and future directions," Images and Vision Computing 31, pp. 120-136, 2013.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 74]


[25] H. Chen, C. Huang, C. Fu, "Hybrid-boost learning for multi-pose face detection and facial expression recognition," Pattern Recognition 41, pp. 1173-1185, 2008.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 44]


[26] X. Huang, G. Zhao, W. Zheng, M. Pietikäinen, "Towards a dynamic expression recognition system under facial occlusion," Pattern Recognition Letters, 33(16), pp. 2181-2191.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 10]


[27] I. B. Ciocoiu, H. N. Costin. "Localized versus locality-preserving subspace projections for face recognition," Journal on Image and Video Processing, 2007(1), pp. 3-3, 2007.
[CrossRef] [Web of Science Record] [SCOPUS Record]


[28] O. Rudovic, M. Pantic, I. (Y.) Patras, "Coupled Gaussian Processes for pose-invariant facial expression recognition," IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 35, no. 6, 2013.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 45]


[29] S. M. Mavadati, M. H. Mahoor, K. Bartlett, P. Trinh, J. F. Cohn, "DISFA: A Spontaneous Facial Action Intensity Database," IEEE Transactions on Affective Computing, vol. 4, no. 2, pp. 151-160, 2013.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 67]


[30] L. Zhang, D. Tjondronegoro, V. Chandran, "Facial expression recognition experiments with data from television broadcasts and the World Wide Web," Image and Vision Computing 32, pp. 107-119, 2014.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 12]


[31] S. Wang, Z. Liu, Z. Wang, G. Wu, P. Shen, S. He, X. Wang, "Analyses of a multi-modal spontaneous facial expression database," IEEE Trans. Affective Computing, vol. 4, issue 1, pp. 34-46, on Jan.-March, 2013.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 9]


[32] X. Zhang, L. Yin, J.F. Cohn, S. Canavan, M. Reale, A. Horowitz, J.M. Girard, "BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database," Image and Vision Computing, vol. 32, no. 10, pp. 692-706, 2014.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 39]


[33] D. McDuff, R. El Kaliouby, T. Senechal, M. Amr, J.F. Cohn, R. Picard, "Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected" In-the-Wild"," In Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on, pp. 881-888, 2013..
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 27]


[34] A. Tcherkassof, D. Dupre, B. Meillon, N. Mandran, M. Dubois, J. Adam, "DynEmo: A video database of natural facial expressions of emotions," The International Journal of Multimedia & Its Applications (IJMA) Vol.5, No.5, pp. 61-80, 2013.
[CrossRef]


[35] I. Sneddon, M. McRorie, G. McKeown, J. Hanratty, "The Belfast Induced Natural Emotion Database," IEEE Transactions on Affective Computing, vol.3, no.1, pp.32,41, Jan.-March 2012.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 26]


[36] A. Dhall, R. Goecke, S. Lucey, T. Gedeon, "Collecting large, richly annotated facial-expression databases from movies," IEEE Multimedia, vol. 19, no. 3, pp. 34-41, July-Sept. 2012,
[CrossRef] [SCOPUS Times Cited 85]


[37] C. Zhan, W. Li, F. Ogunbona, F. Safaei, "A Real-Time Facial Expression Recognition System for Online Games," International Journal of Computer Games Technology, vol. 2008, 7 pages, 2008.
[CrossRef]


[38] R. D'Ambrosio, G. Iannello, P. Soda, "Automatic facial expression recognition using statistical-like moments," Lecture Notes in Computer Science, pp. 585-594, 2011.
[CrossRef] [SCOPUS Times Cited 4]


[39] F. Abdat, C.Maaoui, A.Pruski, "Human-computer interaction using emotion recognition from facial expression," IEEE UKSim 5th European Symposium on Computer Modeling and Simulation, pp. 196-201, 2011.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 14]


[40] C. Martin, U. Werner, H-M. Gross, "A real-time facial expression recognition system based on active appearance models using gray images and edge images," Proc. 8th IEEE int. Conf. On face and Gesture Recognition (FG'08), Amsterdam, paper no. 299, pp. 6, IEEE, 2008.
[CrossRef] [SCOPUS Times Cited 4]


[41] L. Zhang, D. Tjondronegoro and V. Chandran, "Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition," IEEE International Conference on Multimedia and Expo, 2012.
[CrossRef] [SCOPUS Times Cited 7]


[42] L. Zhang, D. Tjondronegoro, V. Chandran, J. Eggink, "Towards robust automatic affective classification of images using facial expressions for practical applications," Multimedia Tools and Applications, pp. 1-27, Springer International Publishing, 2015.
[CrossRef] [Web of Science Record] [SCOPUS Record]


[43] M. Khademit, M. T. Manzuri, M. H. Kiapour, M. Safayabu, M.Shojaei, "Facial expression representation and recognition using 2DHLDA, Gabor Walvelets and Ensemble Learning," 2011. [Persistent URL]

[44] Y. Cheon, D. Kim, "Natural facial expression recognition using differential-AAM and manifold learning," Pattern Recognition 42, 1300-1350, 2009.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 82]


[45] R. A. Khan, A. Meyer, H. Konik, S. Bouakaz, "Framework for reliable, real-time facial expression recognition for low resolution images," Pattern Recognition Letters 34, pp. 1159-1168, 2013.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 34]


[46] E. Sariyanidi, H. Gunes, M. Gökmen, A. Cavallaro, "Local Zernike moment representations for facial affect recognition," In Proceedings of the British Machine Vision Conference, pp.108.1-108.13, BMVA Press, 2013.
[CrossRef] [Web of Science Record]


[47] M. Zhang, D.J. Lee, A. Desai, K.D. Lillywhite, B.J. Tippetts, "Automatic Facial Expression Recognition Using Evolution-Constructed Features," In Advances in Visual Computing, vol. 8888, pp. 282-291, Springer International Publishing, 2014.
[CrossRef]


[48] J. Sung, D. Kim, "Real-time facial expression using STAAM and layered GDA classifier," Image and Vision Computing 27(9), pp. 1313-1325, 2009.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]


[49] C. Fahn, M. Wu and C. Kao, "Real-time facial expression recognition in image sequences using an AdaBoost-based multi-classifier," Proceedings: APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, Annual Summit and Conference, pp. 8-17, 2009. [Handle]

[50] C. Loconsole, D. Chiaradia, V. Bevilacqua, A. Frisoli, "Real-Time Emotion Recognition: An Improved Hybrid Approach for Classification Performance," In Intelligent Computing Theory, pp. 320-331, Springer International Publishing, 2014.
[CrossRef] [SCOPUS Times Cited 1]


[51] Noldus Information Technology, "FaceReader methodology"-White Paper based on FaceReader 5. Available: http://www.noldus.com, accessed on 3.09.2014.

References Weight

Web of Science® Citations for all references: 3,290 TCR
SCOPUS® Citations for all references: 4,966 TCR

Web of Science® Average Citations per reference: 65 ACR
SCOPUS® Average Citations per reference: 97 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references background updated on 2017-02-24 19:29 in 330 seconds.




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