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Erschienen in:
Integrating Map-Reduce and Stream-Processing for Efficiency (MRSP) Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Emotion Recognition: The Influence of Texture’s Descriptors on Classification Accuracy

verfasst von : Karolina Nurzynska

Erschienen in: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Verlag: Springer International Publishing

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Abstract

This work describes experiments dedicated to analysis of the descriptive properties of several, most widely applied, texture operators in emotion recognition domain. Many researchers apply Gabor filters, histogram of oriented gradients, or local binary patterns in complex set-ups with different classification approaches and image processing methodologies, but nowhere it was verified, how each part of the system influences the resulting performance. Therefore, several experiments with Cohn-Kanade AU-Coded Facial Expression and Karolinska Directed Emotional Faces Databases were performed. These experiments reviled, that exploiting the histogram of oriented gradients overcomes other texture operators in most cases.

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Vorheriges Kapitel Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data
Nächstes Kapitel The Use of the TGŚP Module as a Database to Identify Breaks in the Work of Mining Machinery
Literatur
1.
Abdat, F., Maaoui, C., Pruski, A.: Human-computer interaction using emotion recognition from facial expression. In: 2011 Fifth UKSim European Symposium on Computer Modeling and Simulation (EMS), pp. 196–201, November 2011
2.
Carcagni, P., del Coco, M., Leo, M., Distante, C.: Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus 4, 645 (2015)CrossRef
3.
4.
Cohn, J., Zlochower, A., Lien, J., Kanade, T.: Automated face analysis by feature point tracking has high concurrent validity with manual facs coding. Psychophysiology 36(2), 35–43 (1999)CrossRef
5.
Dahmane, M., Meunier, J.: Emotion recognition using dynamic grid-based HoG features. In: 2011 Face and Gesture, pp. 884–888, March 2011
6.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) - Volume 1 - Volume 01, pp. 886–893. IEEE Computer Society, Washington, DC, USA (2005). http://​dx.​doi.​org/​10.​1109/​CVPR.​2005.​177
7.
Darwin, C.: The Expression of the Emotions in Man and Animals. John Murray, London (1872). freeman #1141CrossRef
8.
9.
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124–129 (1971)CrossRef
10.
Ekman, P., Friesen, W.V.: Pictures of Facial Affect. Consulting Psychologists Press, Palo Alto (1976)
11.
Gu, W., Xiang, C., Venkatesh, Y.V., Huang, D., Lin, H.: Facial expression recognition using radial encoding of local gabor features and classifier synthesis. Pattern Recogn. 45(1), 80–91 (2012)CrossRef
12.
13.
14.
Kasprowski, P.: Mining of eye movement data to discover people intentions. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 355–363. Springer, Cham (2014). doi:10.​1007/​978-3-319-06932-6_​34 CrossRef
15.
Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W., Wróbel, M.R.: Emotion recognition and its applications. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T., Wtorek, J. (eds.) Human-Computer Systems Interaction: Backgrounds and Applications 3. AISC, vol. 300, pp. 51–62. Springer, Cham (2014). doi:10.​1007/​978-3-319-08491-6_​5
16.
Li, J., Lam, E.Y.: Facial expression recognition using deep neural networks. In: IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6, September 2015
17.
Lundqvist, D., Flykt, A., Öhman, A.: The Karolinska Directed Emotional Faces - KDEF. CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet (1998)
18.
Luoh, L., Huang, C.C., Liu, H.Y.: Image processing based emotion recognition. In: 2010 International Conference on System Science and Engineering, pp. 491–494, July 2010
19.
Nurzynska, K., Smolka, B.: Smiling and neutral facial display recognition with the local binary patterns operator. J. Med. Imaging Health Inf. 5(6), 1374–1382 (2015)CrossRef
20.
Nurzynska, K., Smolka, B.: PCA application in classification of smiling and neutral facial displays. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 398–407. Springer, Cham (2015). doi:10.​1007/​978-3-319-18422-7_​35
21.
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRef
22.
Ojala, T., Pietikäinen, M., Mäenpää, T.: A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: Singh, S., Murshed, N., Kropatsch, W. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 399–408. Springer, Heidelberg (2001). doi:10.​1007/​3-540-44732-6_​41 CrossRef
23.
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
24.
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, vol. 40. Springer, Heidelberg (2011)
25.
Sadeghi, H., Raie, A.A., Mohammadi, M.R.: Facial expression recognition using geometric normalization and appearance representation. In: 8th Iranian Conference on Machine Vision and Image Processing (MVIP), pp. 159–163, September 2013
26.
Salmam, F.Z., Madani, A., Kissi, M.: Facial expression recognition using decision trees. In: 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pp. 125–130, March 2016
27.
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
28.
Szwoch, M.: On facial expressions and emotions RGB-D database. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 384–394. Springer, Cham (2014). doi:10.​1007/​978-3-319-06932-6_​37 CrossRef
29.
Tsai, H.H., Lai, Y.S., Zhang, A.Y.C.: Using SVM to design facial expression recognition for shape and texture features. In: 2010 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2697–2704, July 2010
30.
31.
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRef
Metadaten
Titel
Emotion Recognition: The Influence of Texture’s Descriptors on Classification Accuracy
verfasst von
Karolina Nurzynska
Copyright-Jahr
2017
Buch
Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Print ISBN: 978-3-319-58273-3

Electronic ISBN: 978-3-319-58274-0

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-58274-0_34

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