Abstract
Facial nerve palsy induces a weakness or loss of facial expression through damage of the facial nerve. A quantitative and reliable assessment system for facial nerve palsy is required for both patients and clinicians. In this study, we propose a rapid and portable smartphone-based automatic diagnosis system that discriminates facial nerve palsy from normal subjects. Facial landmarks are localized and tracked by an incremental parallel cascade of the linear regression method. An asymmetry index is computed using the displacement ratio between the left and right side of the forehead and mouth regions during three motions: resting, raising eye-brow and smiling. To classify facial nerve palsy, we used Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), and Leave-one-out Cross Validation (LOOCV) with 36 subjects. The classification accuracy rate was 88.9%.
Highlights
Facial nerve palsy is a nervous system disorder where there is loss of voluntary muscle movement in a patient’s face caused by nerve damage
The asymmetric index was calculated using the displacement of shape point sets that correspond to the eye-brows and mouth regions while the participants performed facial movements
The images were resized to 540 × 960 to reduce processing time, and asymmetry indices were extracted from the forehead and mouth regions
Summary
Facial nerve palsy is a nervous system disorder where there is loss of voluntary muscle movement in a patient’s face caused by nerve damage. This drawback introduces errors in calculating the asymmetric index as well as low reproducibility In response to these disadvantages, most recent studies have proposed video-based systems. Wang et al proposed an automatic recognition method from six facial actions using active shape models plus Local Binary Patterns (ASMLBP) [3] They used images, not videos, and only recognized those patterns of facial movements required to evaluate the diagnosis of facial paralysis. Et al used Active Appearance Models (AAMs) for facial feature localization and extracted the distance between the corners of the mouth and mean smile as features [6] They used a synthesized dataset that was not from real-world data and assessed paralysis of smiling function. To diagnose facial nerve palsy, we use incremental training of discriminative models to detect facial shape points and extract those features that represent face asymmetry using shape points
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