Abstract
Facial paralysis is a disease that face can not do normal movement on the malfunctioned side. This paper proposes a novel two-stage method for automatically assessing the severity of facial paralysis in a coarse to fine manner. In the first stage, the method coarsely determines whether the query face has severe or mild facial paralysis by analyzing the symmetry of the face under neutral expression and the appearance of the closed eye on the malfunctioned side of the face. In the second stage, the face of severe facial paralysis is further classified into two levels by analyzing the motion feature in showing teeth, while the face of mild facial paralysis is classified into four levels by analyzing the motion feature in showing teeth and raising eyebrows. In both stages, support vector machines (SVMs) are employed to classify the face into different facial paralysis severity levels based on different features. The final assessment is obtained by fusing the results of the multiple SVMs. Evaluation experiments on a database collected by ourselves obtain promising results and prove the effectiveness of fusing the results of multiple classifiers that are based on different features.
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References
Anguraj, K., Padma, S.: Evaluation and severity classification of facial paralysis using salient point selection algorithm. Int. J. Comput. Appl. 123(7), 23–29 (2015)
Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3D face reconstruction and dense alignment with position map regression network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 534–551 (2018)
He, S., Soraghan, J.J., O’Reilly, B.F., Xing, D.: Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. IEEE Trans. Biomed. Eng. 56(7), 1864–1870 (2009)
House, J.W., Brackmann, D.E.: Facial nerve grading system. Laryngoscope 93(8), 1056–1069 (2010)
Liu, X., Dong, S., An, M., Bai, L., Luan, J.: Quantitative assessment of facial paralysis using infrared thermal imaging. In 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 106–110. IEEE (2015)
Modersohn, L., Denzler, J.: Facial paresis index prediction by exploiting active appearance models for compact discriminative features. In: VISIGRAPP (4: VISAPP), pp. 271–278 (2016)
Hung Ngo, T., Chen, Y.-W., Seo, M., Matsushiro, N., Xiong, W.: Quantitative analysis of facial paralysis based on three-dimensional features. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1319–1323. IEEE (2016)
Hung Ngo, T., Seo, M., Matsushiro, N., Chen, Y.-W.: Evaluation of facial paralysis based on spatial features of filtered images. Int. J. Biosci. Biochem. Bioinform. 6(1), 1 (2016)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 7, 971–987 (2002)
Wang, T., Dong, J., Sun, X., Zhang, S., Wang, S.: Automatic recognition of facial movement for paralyzed face. Bio-Med. Mat. Eng. 24(6), 2751–2760 (2014)
Zhang, D.: A method of selecting acupoints for acupuncture treatment of peripheral facial paralysis by thermography. Am. J. Chin. Med. 35(06), 967–975 (2007)
Acknowledgment
This work is supported by the National Natural Science Foundation of China (61773270, 61703077), the Miaozi Key Project in Science and Technology Innovation Program of Sichuan Province, China (No. 2017RZ0016), and Chinese Medicine Science and Technology Research Project of Sichuan Provincial Traditional Chinese Medicine Administration(2018LC031).
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Li, P. et al. (2019). A Two-Stage Method for Assessing Facial Paralysis Severity by Fusing Multiple Classifiers. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_26
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DOI: https://doi.org/10.1007/978-3-030-31456-9_26
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