Abstract
Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems.
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References
Abidin Z, Alamsyah A (2015) Wavelet based approach for facial expression recognition. Int J Adv Intell Inf 1(1):7–14
Aleix M (1998) Martinez. The ar face database. CVC Technical Report
Bartlett MS, Littlewort G, Fasel I, Movellan JR (2003) Real time face detection and facial expression recognition: Development and applications to human computer interaction. In: Conference on computer vision and pattern recognition workshop, 2003. CVPRW’03, vol 5, pp 53–53. IEEE
Bartlett MS, Littlewort G, Frank M, Lainscsek C, Fasel I, Movellan J (2005) Recognizing facial expression: machine learning and application to spontaneous behavior. In: IEEE Computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 2, pp 568–573. IEEE
Bettadapura V (2012) Face expression recognition and analysis: the state of the art. arXiv:1203.6722
Bioid face db - humanscan ag, switzerland. https://www.bioid.com/About/BioID-Face-Database. Accessed: 2014-12-15
Chen L, Man H, Nefian AV (2005) Face recognition based on multi-class mapping of fisher scores. Pattern Recog 38(6):799–811
Dantcheva A, Chen C, Ross A (2012) Can facial cosmetics affect the matching accuracy of face recognition systems?. In: 2012 IEEE Fifth international conference on biometrics: theory, applications and systems (BTAS), pp 391–398. IEEE
Face recognition and artificial vision group frav2d face database. http://www.frav.es/index.php/en/. Accessed: 2014-12-15
Fotosizer software. http://www.gomlab.com/eng/. Accessed: 2014-08-30
Fotosizer software. http://www.fotosizer.com/Download.aspx. Accessed: 2015-02-20
Garris MD (1994) Design, collection, and analysis of handwriting sample image databases. Encyclop Comput Sci Technol 31(16):189–213
Georghiades AS, Belhumeur PN, Kriegman D (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660
Grgic M, Delac K, Grgic S (2011) Scface–surveillance cameras face database. Multi Tools Appl 51(3):863–879
Gross R, Matthews I, Cohn J, Kanade T, Baker S (2010) Multi-pie. Image Vis Comput 28(5):807–813
Happy S, Routray A (2015) Automatic facial expression recognition using features of salient facial patches. IEEE Trans Affect Comput 6(1):1–12
Happy SL, Routray A (2015) Robust facial expression classification using shape and appearance features. In: 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), pp 1–5. IEEE
Jabid T, Md HK, Chae O (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32(5):784–794
Jain V, Mukherjee A (2002) The indian face database. http://vis-www.cs.umass.edu/-vidit/IndianFaceDatabase
Kabir Md H, Jabid T, Chae O (2012) Local directional pattern variance (ldpv): a robust feature descriptor for facial expression recognition. Int Arab J Inf Technol 9(4):382–391
Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Fourth IEEE international conference on automatic face and gesture recognition, 2000. Proceedings, pp 46–53. IEEE
Kang D, Han H, Anil KJ, Lee S-W (2014) Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching. Pattern Recogn 47(12):3750–3766
Kasinski A, Florek A, Schmidt A (2008) The put face database. Image Process Commun 13(3-4):59–64
Lisetti CL, LeRouge C (2004) Affective computing in tele-home health: design science possibilities in recognition of adoption and diffusion issues. In: Proceedings 37th IEEE Hawaii international conference on system sciences, Hawaii, USA
Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 94–101. IEEE
Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings, pp 200–205. IEEE
Marszalec E, Martinkauppi B, Soriano M, Pietika M et al (2000) Physics-based face database for color research. J Electron Imaging 9(1):32–38
Moore S, Bowden R (2009) The effects of pose on facial expression recognition. In: Proceedings of the British machine vision conference, pp 1–11
Nagaraja S, Prabhakar CJ (2014) Extraction of curvelet based rlbp features for representation of facial expression. In: 2014 international conference on contemporary computing and informatics (IC3I), pp 845–850. IEEE
Qi J, Gao X, He G, Luo Z, Yi W (2015) Multi-layer sparse representation for weighted lbp-patches based facial expression recognition. Sensors 15(3):6719–6739
Rivera AR, Castillo R, Chae O (2013) Local directional number pattern for face analysis: Face and expression recognition. IEEE Trans Image Process 22(5):1740–1752
Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, 1994, pp 138–142. IEEE
Shbib R, Zhou S (2015) Facial expression analysis using active shape model. International Journal of Signal Processing, Image Processing and Pattern Recognition 8 (1):9–22
Siddiqi MH, Ali R, Idris M, Khan AM, Kim ES, Whang MC, Lee S (2016) Human facial expression recognition using curvelet feature extraction and normalized mutual information feature selection. Multimedia Tools Appl 75(2):935–959
Siddiqi MH, Ali R, Khan AM, Kim ES, Kim GJ, Lee S (2015) Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and non-linear feature selection. Multimedia Systems 21(6):541–555
Siddiqi MH, Ali R, Khan AM, Park Y-T, Lee S (2015) Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE Trans Image Process 24(4):1386–1398
Siddiqi MH, Lee S, Lee Y-K, Khan AM, Truc PTH (2013) Hierarchical recognition scheme for human facial expression recognition systems. Sensors 13(12):16682–16713
Sim T, Baker S, Bsat M (2003) The cmu pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618
Singh R, Vatsa M, Bhatt HS, Bharadwaj S, Noore A, Nooreyezdan SS (2010) Plastic surgery: A new dimension to face recognition. IEEE Trans Inf Forensics Secur 5(3):441–448
Somanath G, Rohith MV, Vadana CK (2011) A dense dataset for facial image analysis. In: 2011 IEEE international conference on computer vision workshops (ICCV Workshops), pp 2175–2182. IEEE
Sung K-K, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51
The color feret database. http://www.nist.gov/itl/iad/ig/colorferet.cfm. Accessed: 2014-12-15
Thomaz CE, Giraldi GA (2010) A new ranking method for principal components analysis and its application to face image analysis. Image Vis Comput 28(6):902–913
Wang S, Liu Z, Lv S, Lv Y, Wu G, Peng P, Chen F, Wang X (2010) A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans Multimedia 12(7):682–691
Wolf L, Hassner T, Maoz I (2011) Face recognition in unconstrained videos with matched background similarity. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR), pp 529–534. IEEE
Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990
Wu X, Zhao J (2010) Curvelet feature extraction for face recognition and facial expression recognition. In: 2010 6th international conference on natural computation (ICNC), vo 3, pp 1212–1216. IEEE
Zhang B, Zhang L, Zhang D, Shen L (2010) Directional binary code with application to polyu near-infrared face database. Pattern Recogn Lett 31(14):2337–2344
Zhang L, Tjondronegoro D (2011) Facial expression recognition using facial movement features. IEEE Trans Affect Comput 2(4):219–229
Zhu Z, Ji Q (2006) Robust real-time face pose and facial expression recovery. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vo 1, pp 681–688. IEEE
Acknowledgments
This research was supported by the MSIP, Korea, under the G-ITRC support program (IITP-2015-R6812-15-0001) supervised by the IITP, and by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2010-0020210).
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Siddiqi, M.H., Ali, M., Abdelrahman Eldib, M.E. et al. Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets. Multimed Tools Appl 77, 917–937 (2018). https://doi.org/10.1007/s11042-016-4321-2
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DOI: https://doi.org/10.1007/s11042-016-4321-2