Classification of facial paralysis based on machine learning techniques (original) (raw)
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Comprehensive assessment of facial paralysis based on facial animation units
PLOS ONE
Quantitative grading and classification of the severity of facial paralysis (FP) are important for selecting the treatment plan and detecting subtle improvement that cannot be detected clinically. To date, none of the available FP grading systems have gained widespread clinical acceptance. The work presented here describes the development and testing of a system for FP grading and assessment which is part of a comprehensive evaluation system for FP. The system is based on the Kinect v2 hardware and the accompanying software SDK 2.0 in extracting the real time facial landmarks and facial animation units (FAUs). The aim of this paper is to describe the development and testing of the FP assessment phase (first phase) of a larger comprehensive evaluation system of FP. The system includes two phases; FP assessment and FP classification. A dataset of 375 records from 13 unilateral FP patients was compiled for this study. The FP assessment includes three separate modules. One module is the...
Automatic facial analysis for objective assessment of facial paralysis
2009
Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance.
FACE, 2021
Machine learning is a rapidly growing subset of artificial intelligence (AI) which involves computer algorithms that automatically build mathematical models based on sample data. Systems can be taught to learn from patterns in existing data in order to make similar conclusions from new data. The use of AI in facial emotion recognition (FER) has become an area of increasing interest for providers who wish to quantify facial emotion before and after interventions such as facial reanimation surgery. While FER deep learning algorithms are less subjective when compared to layperson assessments, the databases used to train them can greatly alter their outputs. There are currently many well-established modalities for assessing facial paralysis, but there is also increasing interest in a more objective and universal measurement system to allow for consistent assessments between practitioners. The purpose of this article is to review the development of AI, examine its existing uses in facial...
BMC Medical Imaging, 2019
Background: Facial paralysis (FP) is a neuromotor dysfunction that losses voluntary muscles movement in one side of the human face. As the face is the basic means of social interactions and emotional expressions among humans, individuals afflicted can often be introverted and may develop psychological distress, which can be even more severe than the physical disability. This paper addresses the problem of objective facial paralysis evaluation. Methods: We present a novel approach for objective facial paralysis evaluation and classification, which is crucial for deciding the medical treatment scheme. For FP classification, in particular, we proposed a method based on the ensemble of regression trees to efficiently extract facial salient points and detect iris or sclera boundaries. We also employ 2 nd degree polynomial of parabolic function to improve Daugman's algorithm for detecting occluded iris boundaries, thereby allowing us to efficiently get the area of the iris. The symmetry score of each face is measured by calculating the ratio of both iris area and the distances between the key points in both sides of the face. We build a model by employing hybrid classifier that discriminates healthy from unhealthy subjects and performs FP classification. Results: Objective analysis was conducted to evaluate the performance of the proposed method. As we explore the effect of data augmentation using publicly available datasets of facial expressions, experiments reveal that the proposed approach demonstrates efficiency. Conclusions: Extraction of iris and facial salient points on images based on ensemble of regression trees along with our hybrid classifier (classification tree plus regularized logistic regression) provides a more improved way of addressing FP classification problem. It addresses the common limiting factor introduced in the previous works, i.e. having the greater sensitivity to subjects exposed to peculiar facial images, whereby improper identification of initial evolving curve for facial feature segmentation results to inaccurate facial feature extraction. Leveraging ensemble of regression trees provides accurate salient points extraction, which is crucial for revealing the significant difference between the healthy and the palsy side when performing different facial expressions.
EURASIP Journal on Image and Video Processing, 2007
Thiran Facial paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective, and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann scale. Experiments show the radial basis function (RBF) neural network to have superior performance.
Introduction of Static and Dynamic Features to Facial Nerve Paralysis Evaluation
Lecture Notes in Electrical Engineering, 2020
This paper presents a novel approach of facial nerve paralysis evaluation system where it includes both static and dynamic features to evaluate the severity level of paralysis and classify the type of paralysis whether it is Upper Motor Neuron (UMN) lesion or Lower Motor Neuron (LMN) lesion. Two assessment proposed in the system, regional assessment and lesion assessment, which used static and dynamic features respectively. Individual score, total score and paralysis score are introduced and experiments reveal that the proposed approach demonstrates till 100% accuracy in classifying the subjects into normal and patient, the level of severity, and also the type of lesion by using the k-NN classifier. The results proved that with more experiments and by increasing the number of the data, the system will become a great aid to clinicians in evaluation of facial nerve paralysis and rehabilitation programs to patients.
Automatic recognition of facial movement for paralyzed face
Bio-medical materials and engineering, 2014
Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results dem...
Evaluation of 3d facial paralysis using fuzzy logic
International Journal of Engineering & Technology
Face recognition are of great interest to researchers in terms of Image processing and Computer Graphics. In recent years, various factors become popular which clearly affect the face model. Which are ageing, universal facial expressions, and muscle movement. Similarly in terms of medical terminology the facial paralysis can be peripheral or central depending on the level of motor neuron lesion which can be below the nucleus of the nerve or supra nuclear. The various medical therapy used for facial paralysis are electroaccupunture, electrotherapy, laser acupuncture, manual acupuncture which is a traditional form of acupuncture. Imaging plays a great role in evaluation of degree of paralysis and also for faces recognition. There is a wide research in terms of facial expressions and facial recognition but limited research work is available in facial paralysis. House- Brackmann Grading system is one of the simplest and easiest method to evaluate the degree of facial paralysis. During e...
Facial Paralysis Detection on Images Using Key Point Analysis
Applied Sciences, 2021
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 suit...