Abstract
Recent research has shown that the deep subspace learning (DSL) method can extract high-level features and better represent abstract semantics of data for facial expression recognition. While significant advances have been made in this area, traditional sparse representation classifiers or collaborative representation classifiers are still predominantly used for classification purposes. In this paper, we propose a two-phase representation classifier (TPRC)-driven DSL model for robust facial expression recognition. First, the DSL-based principal component analysis network is used to extract high-level features of training and query samples. Then, the proposed TPRC uses the Euclidean distance as a measure to determine the optimal training sample features (TSFs) for the query sample feature (QSF). Finally, the TPRC represents the QSF as a linear combination of all optimal TSFs and uses the representation result to perform classification. Experiments based on several benchmark datasets confirm that the proposed model exhibits highly competitive performance.
Similar content being viewed by others
References
Gupta, O., Raviv, D., Raskar, R.: Illumination invariants in deep video expression recognition. Pattern Recognit. 76, 25–35 (2018)
Wang, S.J., Li, B.J., Liu, Y.J., et al.: Micro-expression recognition with small sample size by transferring long-term convolutional neural network. Neurocomputing 312, 251–262 (2018)
Perveen, N., Roy, D., Mohan, C.K.: Spontaneous expression recognition using universal attribute model. IEEE Trans. Image Process. 27, 5575–5584 (2018)
Mohanty, A., Sahay, R.R.: Rasabodha: understanding Indian classical dance by recognizing emotions using deep learning. Pattern Recognit. 79, 97–113 (2018)
Salmam, F.Z., Madani, A., Kissi, M.: Fusing multi-stream deep neural networks for facial expression recognition. Signal Image Video Process. 13, 609–616 (2019)
Zeng, N.Y., Zhang, H., Song, B., Liu, W., Li, Y., Dobaie, A.M.: Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273, 643–649 (2018)
Barros, P., Parisi, G.I., Weber, C., Wermter, S.: Emotion-modulated attention improves expression recognition: a deep learning model. Neurocomputing 253, 104–114 (2017)
He, X., Zhang, W.: Emotion recognition by assisted learning with convolutional neural networks. Neurocomputing 291, 187–194 (2018)
Silver, D., Huang, A., Maddison, C.J.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: IEEE Computer Vision and Pattern Recognition, 07–12 June 2015, Boston, MA, USA, pp. 2892–2900. IEEE
Wright, J., Yang, A., Ganesh, A., Shastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
Yan, Q., Song, N., Huang, R.: Accurate and robust facial expressions recognition by fusing multiple sparse representation based classifiers. Neurocomputing 149, 71–78 (2015)
Shi, Q., Eriksson, A., Hengel, A. V. D., Shen C.: Is face recognition really a compressive sensing problem? In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, vol. 42, pp. 553–560 (2011)
Waqas, J., Yi, Z., Lei, Z.: Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recognit. Lett. 34, 201–208 (2013)
Sun, Z., Hu, Z.P., Wang, M., Zhao, S.H.: Individual-free representation based classification for facial expression recognition. Signal Image Video Process. 11, 597–604 (2017)
Sun, Z., Hu, Z.P., Chiong, R., Wang, M.: An adaptive weighted fusion model with two subspaces for facial expression recognition. Signal Image Video Process. 12, 835–843 (2018)
Fan, X., Tjahjadi, T.: A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. Pattern Recognit. 48, 3407–3416 (2015)
Andre, T.L., Edilson, A., Alberto, F.D.S., Thiago, O.S.: Facial expression recognition with convolutional neural network: coping with few data and the training sample. Pattern Recognit. 61, 610–628 (2017)
Zeng, N., Zhang, H., Song, B., Liu, W., et al.: Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273, 643–649 (2018)
Chan, T.H., Jia, K., Gao, S., et al.: PCANet: a simple deep learning baseline for image classification. IEEE Trans. Image Process. 24, 5017–5032 (2015)
Sun, Z., Hu, Z.P., Chiong, R., Wang, M., He, W.: Combining the kernel collaboration representation and deep subspace learning for facial expression recognition. J. Circuits Syst. Comput. 27, 1850121 (2018)
Sun, Z., Chiong, R., Hu, Z.P.: An extended dictionary representation approach with deep subspace learning for facial expression recognition. Neurocomputing 316, 1–9 (2018)
Zhang, H., Nasrabadi, N.M., Zhang, Y., Huang, T.S.: Multi-view automatic target recognition using joint sparse representation. IEEE Trans. Aerosp. Electron. Syst. 48, 2481–2497 (2012)
Wang, D., Lu, H., Yang, M.H.: Kernel collaborative face recognition. Pattern Recognit. 48, 3025–3037 (2015)
Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: IEEE International Conference on Automatic Face and Gesture Recognition, 14–16 April 1998, Nara, Japan, pp. 200–205. IEEE
Lucey, P., Jeffrey, F. C., 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: IEEE Computer Vision and Pattern Recognition, 13–18 June 2010, San Francisco, CA, USA, pp. 94–101. IEEE
Goeleven, E., De Raedt, R., Leyman, L., Verschuere, B.: The Karolinska directed emotional faces: a validation study. Cogn. Emot. 22, 1094–1118 (2008)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28, 807–813 (2010)
Tian, Y.: Evaluation of face resolution for expression analysis. In: IEEE Computer Vision and Pattern, 27 June 2004–2 July 2004, Yorktown Heights, NY, USA, pp. 82–82. IEEE
Lee, S.H., Plataniotis, K.N., Ro, Y.M.: Intra-class variation reduction using training expression images for sparse representation based facial expression recognition. IEEE Trans. Affect. Comput. 5(3), 340–351 (2014)
Mohammadi, M.R., Fatemizadeh, E., Mahoor, M.H.: PCA-based dictionary building for accurate facial expression recognition via sparse representation. J. Vis. Commun. Image Represent. 25(5), 1082–1092 (2014)
Martin, O., Kotsia, I., Macq, B., Pitas, I.: The eNTERFACE’05 audio-visual emotion database. In: 22nd International Conference on Data Engineering Workshops (ICDEW 2006), 3–7 April 2006, Atlanta, USA
Pfister, T., Li, X., Zhao, G. et al.: Recognising spontaneous facial micro-expression. In: IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6–13
Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Acted facial expressions in the wild database: collecting large, richly annotated facial-expression databases from movies. IEEE Multimed. 19(3), 34–41 (2012)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grants 61071199 and 61771420, as well as the China Postdoctoral Science Foundation Grant 2018M641674 and Doctoral Foundation in Yanshan University of China under Grants BL18033.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sun, Z., Chiong, R., Hu, Z. et al. Deep subspace learning for expression recognition driven by a two-phase representation classifier. SIViP 14, 437–444 (2020). https://doi.org/10.1007/s11760-019-01568-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-019-01568-4