A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

Lou, Jianwen; Yu, Hui; Wang, Fei‐Yue

doi:10.1109/tnsre.2019.2961244

Cited by 22 publications

(21 citation statements)

References 73 publications

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“…acial nerve paralysis (FNP), such as Bell's palsy, is a neuromuscular disorder disease that causes the symptoms of facial weakness, disability of facial expressions and movements [1], [2]. The effects of FNP not only cause physical pain, but also put severe restrictions on social interaction and living quality which is often thought as eccentric disposition, depression and loneliness [3], [4]. Facial function rehabilitation is a long-term process [5].…”

Section: Introductionmentioning

confidence: 99%

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Liu

Xia

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: Introductionmentioning

confidence: 99%

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Liu

Xia

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…These results demonstrated the connection between the activity of facial muscles and changes in the superficial geometry of the face (Cattaneo and Pavesi, 2014). The connection between facial activities and facial geometry also helped in explaining why the function of the facial nerve can be assessed automatically from visual face capture using image processing, computer vision, and machine learning techniques (Guarin et al, 2018;Johnston and De Chazal, 2018;Lou et al, 2020).…”

Section: The Correlation Between Facial Muscle Activation Features and Bell's Palsymentioning

confidence: 78%

Comparison of Facial Muscle Activation Patterns Between Healthy and Bell’s Palsy Subjects Using High-Density Surface Electromyography

Cui

Zhong

Yang

et al. 2021

Front. Hum. Neurosci.

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Facial muscle activities are essential for the appearance and communication of human beings. Therefore, exploring the activation patterns of facial muscles can help understand facial neuromuscular disorders such as Bell’s palsy. Given the irregular shape of the facial muscles as well as their different locations, it should be difficult to detect the activities of whole facial muscles with a few electrodes. In this study, a high-density surface electromyogram (HD sEMG) system with 90 electrodes was used to record EMG signals of facial muscles in both healthy and Bell’s palsy subjects when they did different facial movements. The electrodes were arranged in rectangular arrays covering the forehead and cheek regions of the face. The muscle activation patterns were shown on maps, which were constructed from the Root Mean Square (RMS) values of all the 90-channel EMG recordings. The experimental results showed that the activation patterns of facial muscles were distinct during doing different facial movements and the activated muscle regions could be clearly observed. Moreover, two features of the activation patterns, 2D correlation coefficient (corr2) and Centre of Gravity (CG) were extracted to quantify the spatial symmetry and the location of activated muscle regions respectively. Furthermore, the deviation of activated muscle regions on the paralyzed side of a face compared to the healthy side was quantified by calculating the distance between two sides of CGs. The results revealed that corr2 of the activated facial muscle region (classified into forehead region and cheek region) in Bell’s palsy subjects was significantly (p < 0.05) lower than that in healthy subjects, while CG distance of activated facial region in Bell’s palsy subjects was significantly (p < 0.05) higher than that in healthy subjects. The correlation between corr2 of these regions and Bell’s palsy [assessed by the Facial Nerve Grading Scale (FNGS) 2.0] was also significant (p < 0.05) in Bell’s palsy subjects. The spatial information on activated muscle regions may be useful in the diagnosis and treatment of Bell’s palsy in the future.

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“…Most measures were compared and ratios were computed to obtain a value in the range of [0, 1], where 0 means fairly asymmetric and 1 is assumed to be closer to a healthy face. Before describing our proposed measures, it is worth noticing that the angle between two points was computed according to Equation (1), the slope between points was computed following Equation (3), the Euclidean distance was employed in this work according to Equation (4), and the perimeter of a close segment was computed using Equation (5):…”

Section: Facial Measures Computationmentioning

confidence: 99%

“…With annotated data, any classifier could be trained using the extracted facial measures and the labels to detect facial nerve damage in a new image. Although a variety of automated facial nerve function assessment solutions have been proposed, their outcomes fall into two main categories: (1) non-semantic numerical values quantifying static, dynamic, and synkinetic facial features; (2) semantic grade of facial nerve function designed by the clinician [5]. Most existing solutions belong to the first category; however, most of these solutions stay at the method discussion phase, and only a few of them have been implemented into prototypes-for example, the Emotrics software [6].…”

Section: Introductionmentioning

confidence: 99%

“…Here, the data are facial images from either a healthy subject or a facial palsy patient, and the prediction is a binary decision (healthy or patient) or it could be the grade of facial nerve function. Classifiers for these applications are based on the methods of support vector machine (SVM), artificial neural network (ANN), k-nearest-neighbor (KNN), or hybrid classifier [5]. For a new subject, a typical system extracts computational features from the facial data then calls a pre-trained classifier to map the features to the facial nerve malfunction.…”

Section: Introductionmentioning

confidence: 99%

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Facial Paralysis Detection on Images Using Key Point Analysis

2021

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The inability to move the muscles of the face on one or both sides is known as facial paralysis, which may affect the ability of the patient to speak, blink, swallow saliva, eat, or communicate through natural facial expressions. The well-being of the patient could also be negatively affected. Computer-based systems as a means to detect facial paralysis are important in the development of standardized tools for medical assessment, treatment, and monitoring; additionally, they are expected to provide user-friendly tools for patient monitoring at home. In this work, a methodology to detect facial paralysis in a face photograph is proposed. A system consisting of three modules—facial landmark extraction, facial measure computation, and facial paralysis classification—was designed. Our facial measures aim to identify asymmetry levels within the face elements using facial landmarks, and a binary classifier based on a multi-layer perceptron approach provides an output label. The Weka suite was selected to design the classifier and implement the learning algorithm. Tests on publicly available databases reveal outstanding classification results on images, showing that our methodology that was used to design a binary classifier can be expanded to other databases with great results, even if the participants do not execute similar facial expressions.

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A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

Cited by 22 publications

References 73 publications

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

Comparison of Facial Muscle Activation Patterns Between Healthy and Bell’s Palsy Subjects Using High-Density Surface Electromyography

Facial Paralysis Detection on Images Using Key Point Analysis

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