Paper Info
Title
A Two-Stage Method for Assessing Facial Paralysis Severity by Fusing Multiple Classifiers.
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.
Year
DOI
Venue
2019
10.1007/978-3-030-31456-9_26
BIOMETRIC RECOGNITION (CCBR 2019)
Keywords
Field
DocType
Facial paralysis,House-Brackmann grading system,Facial biometrics,Multiple classifiers
Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Facial paralysis
Conference
Volume
ISSN
Citations 
11818
0302-9743
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Pengfei Li100.34
Shune Tan200.34
Xiurong Zhou300.34
Sicen Yan400.34
Qijun Zhao541938.37
Jicheng Zhang600.34
Zejun Lv700.34