Abstract
Usually, sparse representation is adopted to learn the intrinsic structure in label spaces to fulfil recognition tasks. In this paper, we propose a feature learning scheme based on sparse representation and validate its effectiveness taking facial expression recognition as a multi-instance learning problem. By introducing the sparse constraint with l 1 sparse regularization, the proposed model learns the instance-specific feature based on label variance information. In this paper, we propose two schemes for denoting the label variance in multi-instance facial expression recognition. Experimental analysis shows that the sparse constraint is useful in feature learning when label variance is properly expressed and utilized. We successfully obtain the stable structure in the feature spaces with the sparse representation based on multi-instance feature learning.
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References
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. IVC 27, 803–816 (2009)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. TPAMI 28, 2037–2041 (2006)
Liao, C.T., Chuang, H.J., Duan, C.H., Lai, S.H.: Learning spatial weighting for facial expression analysis via constrained quadratic programming. PR 46, 3103–3116 (2013)
Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P.: A natural visible and infrared facial expression database for expression recognition and emotion inference. Mul 12, 682–691 (2010)
Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., Metaxas, D.N.: Learning active facial patches for expression analysis. In: CVPR, pp. 2562–2569 (2012)
Yang, P., Liu, Q., Metaxas, D.N.: Boosting coded dynamic features for facial action units and facial expression recognition. In: CVPR, pp. 1–6 (2007)
Zhao, G., Pietikäinen, M.: Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. PRL 30, 1117–1127 (2009)
Xiao, R., Zhao, Q., Zhang, D., Shi, P.: Facial expression recognition on multiple manifolds. PR 44, 107–116 (2011)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. TPAMI 31, 210–227 (2009)
He, R., Zheng, W.S., Hu, B.G.: Maximum correntropy criterion for robust face recognition. TPAMI 33, 1561–1576 (2011)
Zhang, M.L., Zhou, Z.H.: Improve multi-instance neural networks through feature selection. NPL 19, 1–10 (2004)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: FGR, pp. 46–53 (2000)
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Fang, Y., Chang, L. (2015). Multi-instance Feature Learning Based on Sparse Representation for Facial Expression Recognition. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_20
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DOI: https://doi.org/10.1007/978-3-319-14445-0_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
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