SVM and Logistic Regression for Facial Palsy Detection Utilizing Facial Landmark Features

Anuja Arora, Anubhav Sinha, Kaushal Bhansali, Rachit Goel, Isha Sharma, Ambikesh Jayal

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

3 Citations (Scopus)

Abstract

Facial Palsy is a problem related to temporary or permanent damage of facial nerve. Conventional technique for facial paralysis is physical detection and manual measurement for reconstruction of facial features in order to provide perfect balance of patient's face. These Conventional techniques need to be strengthen using computational process. The present research work is carried out in this same direction. Facial palsy data collection and in continuation landmark coordination generation are challenging task. Landmark coordination is an input for learning model. Two machine learning models - Support Vector Machine and Logistic Regression are applied and these machine learning models will train the system using generated facial landmark features. The two important tasks for handling the facial palsy detection using machine learning are Landmark feature generation and effective machine learning model training. The outcome for facial palsy detection using support vector machine is better than logistic regression. The average accuracy achieved by support vector machine is 76.87%

Original languageEnglish
Title of host publication2022 14th International Conference on Contemporary Computing, IC3 2022
EditorsSartaj Sahni, Vikas Saxena, Sundaraja Sitharama Iyengar
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Pages43-48
Number of pages6
ISBN (Print)9781450396752
DOIs
Publication statusPublished - 4 Aug 2022
Event14th International Conference on Contemporary Computing, IC3 2022 - Noida, India
Duration: 4 Aug 20226 Aug 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th International Conference on Contemporary Computing, IC3 2022
Country/TerritoryIndia
CityNoida
Period4/08/226/08/22

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