Abstract
Facial expression recognition has been an emerging research area in last two decades. This paper proposes a new hybrid system for automatic facial expression recognition. The proposed method utilizes histograms of oriented gradients (HOG) descriptor to extract features from expressive facial images. Feature reduction techniques namely principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to obtain the most important discriminant features. Finally, the discriminant features are fed to the back-propagation neural network (BPNN) classifier to determine the underlying emotions from expressive facial images. The Extended Cohn-Kanade dataset (CK\(+\)) is used to validate the proposed method. Experimental results indicate that the proposed system provides the better result as compared to state-of-the-art methods in terms of accuracy with the substantially lesser number of features.
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References
Tian, Y.l., Brown, L., Hampapur, A., Pankanti, S., Senior, A., Bolle, R.: Real world real-time automatic recognition of facial expressions. In: Proceedings of IEEE workshop on Performance Evaluation of Tracking and Surveillance (PETS) (2003)
Pantic, M., Rothkrantz, L.J.: Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1424–1445 (2000)
Bettadapura, V.: Face expression recognition and analysis: the state of the art. arXiv preprint arXiv:1203.6722 (2012)
Tian, Y.L., Kanade, T., Cohn, J.F.: Facial expression analysis. In: Handbook of face recognition, pp. 247–275. Springer (2005)
Bartlett, M.S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Fully automatic facial action recognition in spontaneous behavior. In: 7th International Conference on Automatic Face and Gesture Recognition. pp. 223–230. (2006)
Gritti, T., Shan, C., Jeanne, V., Braspenning, R.: Local features based facial expression recognition with face registration errors. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–8. (2008)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). vol. 1, pp. I–511. (2001)
Tsai, H.H., Lai, Y.S., Zhang, Y.C.: Using svm to design facial expression recognition for shape and texture features. In: International Conference on Machine Learning and Cybernetics (ICMLC). vol. 5, pp.2697–2704. (2010)
Chen, J., Chen, D., Gong, Y., Yu, M., Zhang, K., Wang, L.: Facial expression recognition using geometric and appearance features. In: 4th International Conference on Internet Multimedia Computing and Service. pp. 29–33. (2012)
Valstar, M.F., Pantic, M.: Fully automatic recognition of the temporal phases of facial actions. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(1), 28–43 (2012)
Hsieh, C.C., Hsih, M.H., Jiang, M.K., Cheng, Y.M., Liang, E.H.: Effective semantic features for facial expressions recognition using svm. Multimedia Tools and Applications pp. 1–20 (2015)
Chen, J., Chen, Z., Chi, Z., Fu, H.: Facial expression recognition based on facial components detection and hog features. In: International Workshops on Electrical and Computer Engineering Subfields (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, (CVPR). vol. 1, pp. 886–893. IEEE (2005)
Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional datawith application to face recognition. Pattern recognition 34(10), 2067–2070 (2001)
Bishop, C.M.: Pattern recognition and machine learning. Springer (2006)
Haykin, S., Network, N.: A comprehensive foundation. Neural Networks 2 (2004)
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 Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 94–101. (2010)
Saeed, A., Al-Hamadi, A., Niese, R., Elzobi, M.: Frame-based facial expression recognition using geometrical features. Advances in Human-Computer Interaction (2014)
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Kar, N.B., Babu, K.S., Jena, S.K. (2017). Face Expression Recognition Using Histograms of Oriented Gradients with Reduced Features. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_19
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DOI: https://doi.org/10.1007/978-981-10-2107-7_19
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