Abstract
Facial Emotion Recognition (FER) plays an essential role in human-to-human communication and human-to-machine interaction. Based on the analysis of the facial expressions, the machine can understand the emotional status of the human and take suitable actions. A huge amount of works was done by researchers for decades to build FER systems that are able to discriminate facial emotion features and identify their categories. In this paper, a novel FER framework is suggested to overcome the drawbacks of the previous systems. The Extreme Learning Machine (ELM) universal approximation characteristic along with the Improved Black Hole algorithm global search ability are combined and used to classify the facial images. The Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are utilized to reduce the dimensions of the face images and keep the most discriminative features before feeding them into our system. The proposed system is evaluated over Japanese female facial expression (JAFFE), Karolinska directed emotional faces (KDEF), and extended Cohn-Kanade datasets (CK+), and succeeded to achieve an accuracy of more than 90% over all the datasets. The experiments are extended by testing the proposed system over our own designed facial dataset where the acquired accuracy of the LDA-BH-ELM approach reached 77%, 80% over CK+, KDEF datasets respectively. The comparison of results with the previous methods proved the efficacy and effectiveness of the proposed system, and its ability to achieve outstanding performance.
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References
Chen CH (2015) Handbook of pattern recognition and computer vision. World Scientific
Chen C-H, Lee I-J, Lin L-Y (2015) Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Res Dev Disabil 36:396–403
Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, Taylor JG (2001) Emotion recognition in human-computer interaction. IEEE Signal Process Mag 18:32–80
Deeb H, Sarangi A, Mishra D, Sarangi SK (2020) Improved black hole optimization algorithm for data clustering. J King Saud Univ Inf Sci
Deng W-Y, Zheng Q-H, Lian S, Chen L, Wang X (2010) Ordinal extreme learning machine. Neurocomputing 74:447–456
Friesen E, Ekman P (1978) Facial action coding system: a technique for the measurement of facial movement. Palo Alto 3:5
Fu K-S (2019) Applications of pattern recognition. CRC press
Ghojogh B, Crowley M (2019) Linear and quadratic discriminant analysis: tutorial. arXiv Prepr arXiv190602590
Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci (Ny) 222:175–184. https://doi.org/10.1016/j.ins.2012.08.023
Hickson S, Dufour N, Sud A, et al (2019) Eyemotion: classifying facial expressions in VR using eye-tracking cameras. In: 2019 IEEE winter conference on applications of computer vision (WACV). Pp 1626–1635
Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062
Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE cat. No. 04CH37541). Pp 985–990
Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man, Cybern Part B 42:513–529
Huang X, Wu L, Ye Y (2019) A review on dimensionality reduction techniques. Int J Pattern Recognit Artif Intell 33:1950017
Islam B, Mahmud F, Hossain A, et al (2018) Human facial expression recognition system using artificial neural network classification of Gabor feature based facial expression information. In: 2018 4th international conference on electrical engineering and Information & Communication Technology (iCEEiCT). Pp 364–368
Islam B, Mahmud F, Hossain A (2018) Facial region segmentation based emotion recognition using extreme learning machine. In: 2018 international conference on advancement in electrical and electronic engineering (ICAEEE). Pp 1–4
Izard CE (1971) The face of emotion.
Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22:4–37
Jain N, Kumar S, Kumar A, Shamsolmoali P, Zareapoor M (2018) Hybrid deep neural networks for face emotion recognition. Pattern Recogn Lett 115:101–106
Jasiewicz J, Stepinski TF (2013) Geomorphons—a pattern recognition approach to classification and mapping of landforms. Geomorphology 182:147–156
Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc A Math Phys Eng Sci 374:20150202
Kalantarian H, Jedoui K, Washington P, Tariq Q, Dunlap K, Schwartz J, Wall DP (2019) Labeling images with facial emotion and the potential for pediatric healthcare. Artif Intell Med 98:77–86
Khan S, Hussain M, Aboalsamh H, Bebis G (2017) A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimed Tools Appl 76:33–57
Kim J-H, Kim B-G, Roy PP, Jeong D-M (2019) Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE Access 7:41273–41285
Lan Y, Soh YC, Huang G-B (2010) Two-stage extreme learning machine for regression. Neurocomputing 73:3028–3038
Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423
Liu Z-T, Sui G-T, Li D-Y, Tan G-Z (2015) A novel facial expression recognition method based on extreme learning machine. In: 2015 34th Chinese control conference (CCC). Pp 3852–3857
Liu Z-T, Li S-H, Cao W-H et al (2019) Combining 2D gabor and local binary pattern for facial expression recognition using extreme learning machine. J Adv Comput Intell Intell Inform 23:444–455
Lucey P, Cohn JF, Kanade T, et al (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. Pp 94–101
Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from dep. Clin. Neurosci. Psychol. Sect. Karolinska Institutet 91–630
Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings Third IEEE international conference on automatic face and gesture recognition. pp. 200–205
Martinez AM, Kak AC (2001) Pca versus lda. IEEE Trans Pattern Anal Mach Intell 23:228–233
Mehrabian A (2008) Communication without words. Commun Theory 6:193–200
Mollahosseini A, Chan D, Mahoor MH (2016) Going deeper in facial expression recognition using deep neural networks. In: 2016 IEEE winter conference on applications of computer vision (WACV). Pp 1–10
Moore S, Bowden R (2011) Local binary patterns for multi-view facial expression recognition. Comput Vis Image Underst 115:541–558
Paolanti M, Frontoni E (2020) Multidisciplinary pattern recognition applications: a review. Comput Sci Rev 37:100276
Prince SJD, Elder JH (2007) Probabilistic linear discriminant analysis for inferences about identity. In: 2007 IEEE 11th international conference on computer vision. Pp 1–8
Rahulamathavan Y, Phan RC-W, Chambers JA, Parish DJ (2012) Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Trans Affect Comput 4:83–92
Rao Q, Qu X, Mao Q, Zhan Y (2015) Multi-pose facial expression recognition based on SURF boosting. In: 2015 international conference on affective computing and intelligent interaction (ACII). Pp 630–635
Rujirakul K, So-In C (2018) Histogram equalized deep PCA with ELM classification for expressive face recognition. In: 2018 international workshop on advanced image technology (IWAIT). Pp 1–4
Shah JH, Sharif M, Yasmin M, Fernandes SL (2017) Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recogn Lett
Siddiqi MH, Ali R, Khan AM, Young-Tack Park, Sungyoung Lee (2015) Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE Trans Image Process 24:1386–1398
Tsai H-H, Chang Y-C (2018) Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Comput 22:4389–4405
Tzeng D-Y, Berns RS (2005) A review of principal component analysis and its applications to color technology. Color res Appl endorsed by inter-society color Counc colour gr (Great Britain), can Soc color color Sci Assoc Japan. Dutch Soc Study Color Swedish Colour Cent Found Colour Soc 30:84–98
Vikram K, Padmavathi S (2017) Facial parts detection using Viola Jones algorithm. In: 2017 4th international conference on advanced computing and communication systems (ICACCS). Pp 1–4
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. Pp I--I
Wang X, Tang X (2004) Dual-space linear discriminant analysis for face recognition. In: proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004. Pp II--II
Xanthopoulos P, Pardalos PM, Trafalis TB (2013) Linear discriminant analysis. In: Robust data mining. Springer, pp. 27–33
Xie S, Hu H (2018) Facial expression recognition using hierarchical features with deep comprehensive multipatches aggregation convolutional neural networks. IEEE Trans Multimed 21:211–220
Yang B, Chen S (2013) A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120:365–379
Ye J (2005) Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J Mach Learn Res 6:483–502
Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recogn 34:2067–2070
Zhai J, Zang L, Zhou Z (2018) Ensemble dropout extreme learning machine via fuzzy integral for data classification. Neurocomputing 275:1043–1052
Zhan C, Li W, Ogunbona P, Safaei F (2008) A real-time facial expression recognition system for online games. Int J Comput Games Technol 2008:1–7
Zhang R, Lan Y, Huang G, Xu Z-B (2012) Universal approximation of extreme learning machine with adaptive growth of hidden nodes. IEEE Trans Neural Networks Learn Syst 23:365–371
Zhao X, Zhang S (2012) Facial expression recognition based on local binary patterns and least squares support vector machines. Lect Notes Electr Eng 140 LNEE:707–712. https://doi.org/10.1007/978-3-642-27296-7_106
Zhao Y, Chen D (2020) A Facial Expression Recognition Method Using Improved Capsule Network Model Sci Program 2020
Zhou Y, Shi BE (2017) Action unit selective feature maps in deep networks for facial expression recognition. In: 2017 international joint conference on neural networks (IJCNN). Pp 2031–2038
Zhu W, Miao J, Qing L, Huang G-B (2015) Hierarchical extreme learning machine for unsupervised representation learning. In: 2015 international joint conference on neural networks (ijcnn). Pp 1–8
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Deeb, H., Sarangi, A., Mishra, D. et al. Human facial emotion recognition using improved black hole based extreme learning machine. Multimed Tools Appl 81, 24529–24552 (2022). https://doi.org/10.1007/s11042-022-12498-8
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DOI: https://doi.org/10.1007/s11042-022-12498-8