The methods for clinically diagnosing facial paralysis require doctors to possess a high degree of experience and specialized knowledge, often involving subjectivity. However, due to the uneven distribution of medical resources, many facial paralysis patients are unable to receive timely and accurate diagnosis and treatment. Traditional computer-assisted methods place high demands on hardware equipment and lack sufficient intelligence. With the continuous advancement of artificial intelligence, researchers have actively explored intelligent methods for facial paralysis detection. These methods mainly focus on extracting facial features and making judgments based on facial asymmetry, but they struggle to provide a scientific quantitative analysis of the severity of facial paralysis. This study is based on the lightweight network—MobileNetV2. By performing facial detection and processing on input images, it successfully identifies three groups of acupoints related to facial paralysis and conducts quantitative analysis based on this identification. Simultaneously, we have improved the network by constructing a two-stage network similar to object detection and regression, and optimizing the loss function. In the end, we compared the improved model with other mainstream frameworks through experiments. The results demonstrate that our proposed model achieves significant effectiveness in acupoint recognition and maintains low error in quantitative analysis.

Keding Huang, Feiyu Zhu, Jicheng Zhang, Shun’e Tan, Ziyu Li. An Intelligent Facial Palsy Diagnostic System Based on Acupoint Identification. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 9: 81-86. https://doi.org/10.25236/AJCIS.2023.060912.

[1] Reginald F. Baugh et al. Clinical Practice Guideline: Bell’s Palsy[J]. Otolaryngology–Head and Neck Surgery, 2013, 149(2_suppl): P 13-P14. 

[2] House J W, Brackmann D E. Facial nerve grading system [J]. Otolaryngology-head and neck surgery: official journal of American Academy of Otolaryngology-Head and Neck Surgery, 1985, 93(2): 146-148. 

[3] T. H. Ngo, M. Seo, N. Matsushiro and Y. -W. Chen, "Quantitative analysis of facial paralysis based on filters of concentric modulation, " 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China, 2015, pp. 1758-1763, doi: 10. 1109/FSKD. 2015. 7382213. 

[4] A. M. Ridha, W. Shehieb, P. Yacoub, K. Al-Balawneh and K. Arshad, "Smart Prediction System for Facial Paralysis, " 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE), Antalya, Turkey, 2020, pp. 321-327, doi: 10. 1109/ICEEE49618. 2020. 9102600. 

[5] Wang Bolu, Wu Xiaomei. Application of Artificial Intelligence in Facial Paralysis Recognition and Assessment [J]. Chinese Journal of Medical Devices, 2022, 46(1): 6. 

[6] Cootes T F, Taylor C J, Cooper D H, et al. Active shape models-their training and application[J]. Computer vision and image understanding, 1995, 61(1): 38-59. 3

[7] Y. Sun, X. Wang and X. Tang, "Deep Convolutional Network Cascade for Facial Point Detection, " 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, pp. 3476-3483, doi: 10. 1109/CVPR. 2013. 446. 

[8] Zhang K, Zhang Z, Li Z, et al. Joint face detection and alignment using multitask cascaded convolutional networks [J]. IEEE signal processing letters, 2016, 23(10): 1499-1503. 

[9] Kowalski M, Naruniec J, Trzcinski T. Deep alignment network: A convolutional neural network for robust face alignment[C]//Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017: 88-97. 

[10] Zhao Y , Zhang D , Wang Y .Automatic location of facial acupuncture-point based on content of infrared thermal image[C]//International Conference on Computer Science & Education. IEEE, 2010.DOI:10.1109/ICCSE.2010.5593668. 

[11] Chang M, Zhu Q. Automatic location of facial acupuncture-point based on facial feature points positioning[C]//Research Institute of Management Science and Industrial Engineering. Proceedings of 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017). Atlantis Press, 2017:5. 

[12] Wang Qi, Chen Gong, Hu Wenxin, et al. Research on Facial Paralysis Recognition Application Using GANomaly Network [J]. Journal of Software Engineering, 2022, 25(03): 29-33. DOI: 10. 19644/j. cnki. issn2096-1472. 2022. 003. 007. 

[13] Xu Pengfei, Yang Chunlei, Sun Haojie, et al. Facial Paralysis Grading Assessment Method Based on Deep Temporal Features [J/OL]. Acta Automatica Sinica. http://kns. cnki. net/ kcms/detail/ 11. 2019. TP. 20200414. 1951. 005. html. 

[14] Neely J G, Joaquin A H, Kohn L A, et al. Quantitative assessment of the variation within grades of facial paralysis [J]. Laryngoscope, 1996, 106: 438-442

[15] Wang T, Zhang S, Liu L, et al. Automatic evaluation of the degree offacial nerve paralysis [J]. Multimedia Tools & Applications, 2016, 75(19): 11893-11908

[16] Sandler M, Howard A, Zhu M, et al. Mobilenetv 2: Inverted residuals and linear bottlenecks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.