Abstract
Emotion recognition has captured the attention of many researchers these days. However most of the researches have happened in emotion recognition from frontal faces. In real-world conditions, we may not always be able to capture the frontal faces. Hence, emotion recognition from non-frontal faces is the new research area. The Local Binary Patterns (LBP) is an important feature extraction technique. In our effort to find better variant of LBP for non-frontal emotion detection, we have used two possible variants namely Local Gabor Binary Pattern (LGBP) and Diagonal Crisscross Local Binary Pattern (DCLBP). The LGBP is further implemented with and without Angle Classification; which leads to total three methods of feature extraction. These three methods are used to classify the images based on facial emotion expressions. An image is divided into number of blocks. Feature vectors are created by concatenating histograms computed from each sub-block. Multi-class SVMs are used to classify angles and expressions. A comparative analysis of the three methods for non-frontal emotion recognition has been carried out and is presented in this paper. By analyzing different variants of a LBP, we can understand the importance of feature representation in non-frontal emotion recognition. Our experimental studies show that the LGBP with angle classification outperforms other two variants.
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References
Mehrabian, A.: Silent Messages. Wadsworth Publishing Company, Inc., Belmont, CA, (1971)
Tian, Y., Kanade, T., Cohn, J.: Facial expression analysis. In: Handbook of Face Recognition. Springer, (Chapter 11) (2005)
Lyons, J., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21, 1357–1362 (1999)
Gong, S., McOwan, P.W., Shan, C.: Dynamic facial expression recognition using a bayesian temporal manifold model. In: Proceedings of the British Machine Vision Conference, vol. 1, pp. 297–306 (2006)
Liao, S., Fan, W., Chung, A.C.S., Yeung, D.Y.: Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features, In: ICIP, pp. 665–668 (2006)
Shan, C., Gritti, T..: Learning discriminative lbp-histogrambins for facial expression recognition. In: Proceedings of the British Machine Vision Conference (2008)
Moore, S., Bowden, R.: Local binary patterns for multi-view facial expression recognition. Comput. Vis. Image Underst. 115(4), 541–558 (2011)
Hossain, S.: Study on Co-occurrence-based image feature analysis and texture recognition employing diagonal-crisscross local binary pattern. Technical Report. Kyushu Institute of Technology, Japan (2013). http://hdl.handle.net/10228/5282
Tang, H., Hasegawa-Johnson, M., Huang, T.: Non-frontal view facial expression recognition based on ergodic hidden Markov model supervectors. In: 2010 IEEE International Conference on Multimedia and Expo (ICME) (2010)
Lundqvist, D., Flykt, A., Öhman, A.: The Karolinska Directed Emotional Faces—KDEF. In: CD ROM from Department of Clinical Neuroscience, Psychology Section. Karolinska Institute (1998). ISBN 91-630-7164-9
Hu, Y., Zeng, Z., Yin, L., Wei, X., Tu, J., Huang, T.S.: Multi-view facial expression recognition. In: FG2008, 2008 ICPR 2008. 8th IEEE International Conference on Automatic Face and gesture Recognition, Automatic Face & Gesture Recognition (2008)
Hu, Y., Zeng, Z., Yin, L., Wei, X., Tu, J., Huang, T.S.: A study of non-frontal-view facial expressions recognition. In: 19th International Conference on Pattern Recognition. ICPR 2008, pp. 1–4 (2008)
Moore, S., Bowden, R.: The effects of pose on facial expression recognition. In: Proceedings of British Machine Vision Conference (BMVC2009), London, UK (2009)
Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.J.: A 3D facial expression database for facial behavior research. In: 7th International Conference on Automatic Face and Gesture Recognition, 2006. FGR 2006, pp. 211–216. https://doi.org/10.1109/fgr.2006. 6 Apr 2006
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)
Shojaeilangari, S., Yau, W.-Y., Teoh, E.-K.: Pose-invariant descriptor for facial emotion recognition. Mach. Vis. Appl. 27(7), 1063–1070 (2016)
Tian, Y., Brown, L., Hampapur, A., Pankanti, S., Senior, A., Bolle, R.: Real world real-time automatic recognition of facial expressions. In: Proceedings of IEEE Workshop (2003)
Littlewort, G., Bartlett, M.S., Fasel, I., Susskind, J., Movellan, J.: Dynamics of facial expression extracted automatically from video. J. Image Vis. Comput. 615–625 (2004)
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Dosi, H., Keshri, R., Srivastav, P., Agrawal, A. (2020). Comparison Between LGBP and DCLBP for Non-frontal Emotion Recognition. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_27
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DOI: https://doi.org/10.1007/978-981-32-9291-8_27
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