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Automatic feature point detection using deep convolutional networks for quantitative evaluation of facial paralysis.

, , , , and . CISP-BMEI, page 811-814. IEEE, (2016)

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Simulation of Facial Palsy using Conditional Generative Adversarial Networks and Face Shape Normalization., , , and . GCCE, page 793-797. IEEE, (2021)Design of Soft Robotic Actuation for Supporting Eyelid Closure Movement., , and . EMBC, page 2760-2763. IEEE, (2018)Automatic feature point detection using deep convolutional networks for quantitative evaluation of facial paralysis., , , , and . CISP-BMEI, page 811-814. IEEE, (2016)Quantitative analysis of facial paralysis based on limited-orientation modified circular Gabor filters., , , , and . ICPR, page 349-354. IEEE, (2016)Quantitative analysis of facial paralysis based on filters of concentric modulation., , , and . FSKD, page 1758-1763. IEEE, (2015)Quantitative assessment of facial paralysis using local binary patterns and Gabor filters., , , and . SoICT, page 155-161. ACM, (2014)Quantitative analysis of facial paralysis based on three-dimensional features., , , , and . ICIP, page 1319-1323. IEEE, (2016)Quantitative evaluation of facial paralysis using tracking method., , , and . RIVF, page 100-105. IEEE, (2015)Simulation of Facial Palsy using an Improved Cycle GAN and Face Restoration Network., , , and . ICCE, page 1-4. IEEE, (2023)Simulation of Facial Palsy Using an Improved Generative Adversarial Net with Two Discriminators., , , and . GCCE, page 769-772. IEEE, (2022)