Quantitative Assessment of Facial Paralysis Based on Spatiotemporal Features

Ngo, Truc Hung; Chen, Yen‐Wei; Matsushiro, Naoki; Seo, Masataka

doi:10.1587/transinf.2015edp7082

Cited by 8 publications

(1 citation statement)

References 11 publications

(16 reference statements)

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“…In related studies, various approaches like landmarks [4,6], facial action units [7,8], eye movement features [9][10][11], and convolutional neural networks (CNN) [12][13][14] are used in the feature extraction stage from the FP images and videos. This paper uses novel approaches to detect FP and its grade by combining multiple facial features (landmarks, facial action units, and eye movement features) to achieve better FP detection.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Stacking for Grading Facial Paralysis Through Statistical Analysis of Facial Features

Gogu,

Sathe

2024

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In medical diagnostics, the accurate assessment of facial paralysis (FP) represents a significant challenge, necessitating intricate analysis of facial spatial information, notably asymmetry. This condition, characterized by the inability to regulate facial muscles effectively during specific actions, often demands the discernment of clinicians, which lacks a quantitative foundation. In response to this challenge, the present study introduces two innovative models aimed at enhancing the diagnostic process for FP. The first model employs a binary classification framework to differentiate between affected individuals and those without the condition. The second, more complex model, utilizes an ensemble stacking technique to categorize the severity of FP into four distinct grades: normal, mild, moderate, and severe. Data for this analysis was sourced from a collection comprising 21 individuals diagnosed with FP and 20 healthy counterparts, extracted from publicly accessible datasets. Utilizing the OpenFace 2.0 toolkit, three categories of facial features were analyzed: landmarks, facial action units, and eye movement metrics. A comprehensive evaluation was conducted to determine the optimal model through a series of tests that integrated individual and combined facial feature sets alongside dimension reduction techniques. The findings revealed that the Support Vector Machine (SVM) method, applied to the binary classification of FP, attained an accuracy of 97.7%. Conversely, the ensemble stacking approach, incorporating Logistic Regression (LR) and SVM, demonstrated an 88.2% accuracy rate in the grading of FP severity. These outcomes suggest significant potential for the application of such models in telemedicine, facilitating early detection and ongoing remote monitoring of facial nerve functionality, thereby reducing the need for direct patientclinician encounters.

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