Abstract
In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.
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References
Anderson K, McOwan PW (2006) A real-time automated system for recognition of human facial expressions. IEEE Trans Syst Man Cybern B Cybern 36:96–105
Cohn I, Sebe N, Chen L, Garg A, Huang TS (2003) Facial expression recognition from video sequences: temporal and static modeling. Comput Vis Image Underst 91(1–2):160–187
Zhang Y, Ji Q (2003) Facial expression understanding in image sequences using dynamic and active visual information fusion. IEEE International Conference on Computer Vision, vol 2. Nice, France, pp 113–118
Tian YL, Kanade T, Cohn JF (2005) In: Li SZ, Jain AK (eds) Handbook of face recognition. Springer, Heidelberg, pp 247–276
Ekman P, Friesen WV (1971) Constant across cultures in the face and emotion. J Pers Soc Psychol 17(2):124–129
Lajevardi SM, Hussain ZM (2010) Higher order orthogonal moments for invariant facial expression recognition. Digit Signal Process 20(6):1771–1779
Lu X, Wang Y, Jain AK (2003) Combining classifiers for face recognition. Int Conf Multimed Expo 3:13–16
Donato G, Bartlett M, Hager J, Ekman P, Sejnowski T (1999) Classifying facial actions. IEEE Trans Pattern Anal Mach Intell 21(10):974–989
Tian YL, Kanade T, Cohn J (2001) Recognizing action units for facial expression analysis. IEEE Trans Pattern Anal Mach Intell 23(2):97–115
Zhen W, Huang TS (2003) Capturing subtle facial motions in 3d face tracking. Ninth IEEE Int Conf Comput Vis, vol 2. Nice, France, pp 1343–1350
Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with Gabor wavelets. In: IEEE 3rd international conference on automatic face and gesture recognition, pp 200–205
Gu W, Xiang C, Venkatesh YV, Huang D, Lin H (2012) Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recognit 45(1):80–91
Uçar A (2013) Facial expression recognition based on significant face components using steerable pyramid transform. In: International conference on image processing, computer vision and pattern recognition, vol 2. Las Vegas, USA, pp 687–692
Candes EJ, Donoho DL (2000) Curvelets—a surprisingly effective nonadaptive representation for objects with edges. Vanderbilt University Press, Nashville, TN
Mandal T, Wu JQM, Yuan Y (2009) Curvelet based face recognition via dimensional reduction. Signal Process 89(12):2345–2353
Mohammed AA, Minhas R, Wu JQM, Sid-Ahmed MA (2011) Human face recognition based on multidimensional pca and extreme learning machine. Pattern Recognit 44(10–11):2588–2597
Uçar A (2012) Color face recognition based on curvelet transform. In: International conference on image processing, computer vision and pattern recognition, vol 2. Las Vegas, USA, pp 561–566
Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816
Li ZS, Imai J, Kaneko M (2010) Facial expression recognition using facial–component–based bag of words and PHOG descriptors. J Inst Image Inform Telev En 64(2):230–236
Platt JA (1991) Resource-allocating network for function interpolation. Neural Comput 3(2):213–225
Lu Y, Sundararajan N, Saratchandran PA (1997) Sequential learning scheme for function approximation using minimal radial basis function (RBF) neural networks. Neural Comput 9(2):461–478
Huang GB, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans Syst Man Cybern 34(6):2284–2292
Yap KS, Lim CP, Abidin IZ (2008) A hybrid ART-GRNN online learning neural network with a ε-insensitive loss function. IEEE Trans Neural Netw 19(9):1641–1646
Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward network. IEEE Trans Neural Netw 17(6):1411–1423
Lan Y, Soh YC, Huang GB (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72(13–15):3391–3395
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Demir Y, Uçar A (2003) Modelling and simulation with neural and fuzzy-neural networks of switched circuits. COMPEL: Int J Comput Math Electr Electro Eng 22(2):253–272
Choi K, Toh KA, Byun H (2012) Incremental face recognition for large-scale social network services. Pattern Recognit 45(8):2868–2883
Uçar A (2014) Color face recognition based on steerable pyramid transform and extreme learning machines. Sci World J 2014:1–45. Article Id: 628494
Uçar A, Yavşan E (2014) Behavior learning of memristor—based chaotic circuit by extreme learning machines. Turk J Elec Eng Comp Sci. doi:10.3906/elk-1304-248
Li G, Liu M, Dong M (2010) A new online learning algorithm for structure-adjustable extreme learning machine. Comput Math Appl 60(3):377–389
Uçar A, Demir Y, Güzeliş C (2014) A penalty function method for designing efficient robust classifiers with input–space optimal separating surfaces. Turk J Elec Eng Comp Sci. doi:10.3906/elk-1301-190
Haykin S (2008) Neural networks and learning machines, 3rd edn. Prentice Hall, New Jersey, USA
Uçar A, Demir Y, Güzeliş C (2006) A new formulation for classification by ellipsoids. In: Savacı FA (ed) TAINN 2005. LNAI, vol 3949. Springer, Heidelberg, pp 100–106
Do MN, Vetterli M (2003) The finite ridgelet transform for image representation. IEEE Trans Image Process 12(1):16–28
Candes EJ, Demanet L, Donoho DL, Ying L (2006) Fast Discrete curvelet transforms. Multiscale Model Simul 5(3):861–899
Viola P, Jones M (2004) Robust real–time face detection. Int J Comput Vision 57(2):137–154
Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comp Vis Graph 39(3):355–368
Lyons M, Budynek J, Akamatsu S (1999) Automatic classification of single facial images. IEEE Trans Pattern Anal Mach Intell 21(12):1357–1362
Kanade T, Cohn JF, Yingli T (2000) Comprehensive database for facial expression analysis. In: IEEE 4th international conference on automatic face and gesture recognition. Pittsburgh, PA, USA, pp 46–53
Blake CL, Merz CJ (1998) UCI Repository of machine learning databases. http://archive.ics.uci.edu/ml/datasets.html, Department of Information and Computer Science, University of California, Irvine
Zhang L, Tjondronegoro D (2011) Facial expression recognition using facial movement features. IEEE Trans Affect Compt 2(4):219–229
Kyperountas M, Tefas A, Pitas I (2010) Salient feature and reliable classifier selection for facial expression classification. Pattern Recognit 43(3):972–986
Zhengdong C, Bin S, Xiang F, Yu-Jin Z (2008) Automatic coefficient selection in weighted maximum margin criterion. In: 19th International conference on pattern recognition. Tampa, FL, pp 1–4
Horikawa Y (2007) Facial expression recognition using KCCA with combining correlation kernels and Kansei information. In: International conference on computational science and its applications. Kuala Lampur, Malaysian, pp 489–498
Bin J, Guo-Sheng Y, Huan-Long Z (2008) Comparative study of dimension reduction and recognition algorithms of DCT and 2DPCA. In: International conference on machine learning and cybernetics. Kunming, China, pp 407–410
Wong J, Cho SA (2010) Face emotion tree structure representation with probabilistic recursive neural network modeling. Neural Comput Appl 19(1):33–54
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Uçar, A., Demir, Y. & Güzeliş, C. A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering. Neural Comput & Applic 27, 131–142 (2016). https://doi.org/10.1007/s00521-014-1569-1
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DOI: https://doi.org/10.1007/s00521-014-1569-1