Abstract
Facial Expression Recognition (FER) has become one of the most popular areas of research in computer vision and biometrics authentication and it has achieved a lot of enthusiasm from researchers. The Vision based Facial Expression Recognition system intends to classify the facial expression of a given image. In this paper, the proposed system automatically classifies the facial expression. The system is composed of feature extraction and expression classification. In preprocessing, Hybrid filter (Median and Gabor) and Histogram Equalizations, is used to reduce noise and enhance images. Feature extraction is to extract feature vectors from face images using the Eigenfaces approach, based on Principal Component Analysis (PCA). To classify facial expression, extracted feature vectors are fed into a Multiclass Support Vector Machine (Multi-SVM) classifier. Experiments are performed on the standard dataset of the Japanese Female Facial Expression (JAFFE) and achieved 80% accuracy. The proposed system showed satisfying performance comparing with other methods and effects state-of-the-art performance on the JAFFE dataset.
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References
Mehrabian, A.: Communication without words. Psychol. Today 2(4), 53–56 (1968)
Ekman, P., Friesen, W.V., Hager, J.C.: Facial Action Coding System. Consulting Psychologists Press, Palo Alto, CA (1978)
Turk, M.A., Pentland, AP.: Eigenfaces for recognition computer vision and pattern recognition. In: Proceedings of CVPR, pp. 586–591 (1991). https://doi.org/10.1109/cvpr.1991.139758
Bian, Z., Zhang, X.: Pattern Recognition, 2nd edn. Tsinghua University Press, Beijing (2000)
Jonsson, K., Kittler, J.: Support vector machine for face authentication. Image Vis. Comput. 20(5–6), 369–375 (2002). https://doi.org/10.1016/s0262-8856(02)00009-4
Jonsson, K., Matas, J.: Learning support vectors for face verification and recognition. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition (CAFGR), pp. 208–213. France (2000). https://doi.org/10.1109/afgr.2000.840636
Dea, A., Sahaa, A., Dr. Palb, M.C.: A humanfacial expression recognition model based on eigen face approach. In: International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 282–289 (2015). https://doi.org/10.1016/j.procs.2015.03.142
Jameel, R., Singhal, A., Bansal, A.: A comprehensive study on facial expressions recognition techniques. In: 6th International Conference on Cloud System and Big Data Engineering, pp. 478–483. IEEE, India (2016). https://doi.org/10.1109/confluence.2016.7508167
Shan, K., Guo, J., You, W., Lu, D., Bie, R.: Automatic facial expression recognition based on a deep convolutional-neural-network structure. In: 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 123–128. IEEE, UK (2017). https://doi.org/10.1109/sera.2017.7965717
Islam, B., Mahmud, F., Hossain, A.: Facial expression region segmentation based approach to emotion recognition using 2D gabor filter and multiclass support vector machine. In: 21st International Conference of Computer and Information Technology (ICCIT). IEEE, Bangladesh (2018). https://doi.org/10.1109/iccitechn.2018.8631922
Meng, D., Peng, X., Wang, K., Qiao, Y.: Frame attention networks for facial expression recognition in videos. In: IEEE International Conference on Image Processing (ICIP), pp. 3866–3870. IEEE, Taiwan (2019). https://doi.org/10.1109/icip.2019.8803603
Maw, H.M., Lin, K.Z., Mon, M.T.: Preprocessing techniques for face and facial expression recognition. In: 33rd International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 377–380. Thailand (2018)
Murthy, G.R.S., Jadon, R.S.: Effectiveness of eigenspaces for facial expressions recognition. Int. J. Comput. Theory Eng. 1(5), 638–642 (2009)
Dr. Ghadekar, P.P., Alrikabi, H.A., Dr. Chopade, N.B.: Efficient face and facial expression recognition model. In: International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1–8. IEEE, India (2016). https://doi.org/10.1109/iccubea.2016.7860053
Bhat, A., Veigas, J.P.: Efficient implementation on human face recognition under various expressions using LoG, LBP and SVM. Int. J. Eng. Sci. Comput. (IJESC) 7(7), 14052–14055 (2017)
The Japanese Female Facial Expression (JAFFE) Database. http://www.kasrl.org/jaffe.html
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Maw, H.M., Thu, S.M., Mon, M.T. (2020). Vision Based Facial Expression Recognition Using Eigenfaces and Multi-SVM Classifier. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_54
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DOI: https://doi.org/10.1007/978-3-030-63119-2_54
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