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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Facial Expression Recognition by Fusing Gabor and Local Binary Pattern Features

verfasst von : Yuechuan Sun, Jun Yu

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

Obtaining effective and discriminative facial appearance descriptors is a challenging task for facial expression recognition (FER). In this paper, a new FER method which combines two of the most successful facial appearance descriptors, namely Gabor filters and Local Binary Patterns (LBPs), is proposed considering that the former one can represent facial shape and appearance over a broader range of scales and orientations while the latter one can capture subtle appearance details. Firstly, feature vectors of Gabor and LBP representations are generated from the preprocessed face images respectively. Secondly, feature fusion is applied to combine these two vectors and dimensionality reduction is conducted. Finally, the Support Vector Machine (SVM) is adopted to classify prototypical facial expressions using still images. The experimental results on the CK+ database demonstrate that the proposed method promotes the performance compared with that using Gabor or LBP descriptor alone, and outperforms several other methods.

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Vorheriges Kapitel Exploring Large Movie Collections: Comparing Visual Berrypicking and Traditional Browsing
Nächstes Kapitel Frame-Independent and Parallel Method for 3D Audio Real-Time Rendering on Mobile Devices
Literatur
1.
Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)CrossRef
2.
Chew, S.W., Lucey, P., Lucey, S., Saragih, J., Cohn, J.F., Sridharan, S.: Person-independent facial expression detection using constrained local models. In: IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, pp. 915–920 (2011)
3.
Jeni, L.A., Lőrincz, A., Nagy, T., Palotai, Z., Sebők, J., Szabó, Z., Takács, D.: 3D shape estimation in video sequences provides high precision evaluation of facial expressions. Image Vis. Comput. 30(10), 785–795 (2012)CrossRef
4.
Rivera, A.R., Castillo, J.R., Chae, O.O.: Local directional number pattern for face analysis: face and expression recognition. IEEE Trans. Image Process. 22(5), 1740–1752 (2013). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRef
5.
Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)CrossRef
6.
Donato, G., Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J.: Classifying facial actions. IEEE Trans. Pattern Anal. Mach. Intell. 21(10), 974–989 (1999)CrossRef
7.
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)CrossRef
8.
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATH
9.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
10.
Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649 (2012)
11.
Liu, M., Li, S., Shan, S., Wang, R., Chen, X.: Deeply learning deformable facial action parts model for dynamic expression analysis. In: Asian Conference on Computer Vision, pp. 143–157 (2015)
12.
Jung, H., Lee, S., Yim, J., Park, S., Kim, J.: Joint fine-tuning in deep neural networks for facial expression recognition. In: The IEEE International Conference on Computer Vision (ICCV), pp. 2983–2991 (2015)
13.
Liu, M., Li, S., Shan, S., Chen, X.: Au-aware deep networks for facial expression recognition. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6 (2013)
14.
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. I-511 (2001)
15.
Yang, P., Shan, S., Gao, W., Li, S.Z., Zhang, D.: Face recognition using ada-boosted gabor features. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 356–361 (2004)
16.
Abdulrahman, M., Gwadabe, T.R., Abdu, F.J., Eleyan, A.: Gabor wavelet transform based facial expression recognition using PCA and LBP. In: 2014 22nd Signal Processing and Communications Applications Conference (SIU), pp. 2265–2268 (2014)
17.
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRef
18.
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
19.
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRef
20.
Yang, J., Yang, J.Y., Zhang, D., Lu, J.F.: Feature fusion: parallel strategy vs. serial strategy. Pattern Recogn. 36(6), 1369–1381 (2003)CrossRefMATH
21.
Mangai, U.G., Samanta, S., Das, S., Chowdhury, P.R.: A survey of decision fusion and feature fusion strategies for pattern classification. IETE Tech. Rev. 27(4), 293–307 (2010)CrossRef
22.
Ekman, P., Friesen, W.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists, San Francisco (1978)
23.
Lucey, P., Cohn, J.F., 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 Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 94–101 (2010)
24.
Jain, S., Hu, C., Aggarwal, J.K.: Facial expression recognition with temporal modeling of shapes. In: IEEE International Conference on Computer Vision Workshops, pp. 1642–1649 (2011)
25.
Zhang, W., Shan, S., Gao, W., Chen, X.: Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Tenth IEEE International Conference on Computer Vision, pp. 786–791 (2005)
26.
Tan, X., Triggs, B.: Fusing Gabor and LBP feature sets for kernel-based face recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 235–249. Springer, Heidelberg (2007). doi:10.​1007/​978-3-540-75690-3_​18 CrossRef
27.
Zavaschi, T.H.H., Britto, A.S., Oliveira, L.E.S., Koerich, A.L.: Fusion of feature sets and classifiers for facial expression recognition. Expert Syst. Appl. 40(2), 646–655 (2013)CrossRef
Metadaten
Titel
Facial Expression Recognition by Fusing Gabor and Local Binary Pattern Features
verfasst von
Yuechuan Sun
Jun Yu
Copyright-Jahr
2017
Buch
MultiMedia Modeling

Print ISBN: 978-3-319-51813-8

Electronic ISBN: 978-3-319-51814-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-51814-5_18

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