Correlation of Objective Assessment of Facial Paralysis with House-Brackmann Score

This article illustrated a brief review of some objective methods in assessing facial nerve function for facial nerve paralysis which were correlated with House-Brackmann Grading System (HBGS). A rigorous search of online databases such as Springer, Elsevier and IEEE was conducted from June, 2015 to November, 2016 to discover and analyze the previous works in facial nerve assessment methods for facial paralysis. Several domains such as facial grading system and methods used to evaluate the facial nerve function were extracted for further analysis. Different keywords were used to acquire the studies based on the desire criteria. A total of 8 articles were identified and were analyzed for inclusion in this search. In conclusion, this review has presented an initial overview for further improvements in objective facial nerve assessment which has to be correlated with subjective assessment to make it more reliable and useful in clinical practice.

Automatic facial analysis for objective assessment of facial paralysis

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

Fluctuating facial asymmetry and visual perceptive background during a tissue diagnostic histopathological

Background. Fluctuating facial asymmetry (FFA) is accentuated throughout life and has perceptual psychological implications; tissue diagnosis shows interindividual differences at first glance, for example, in the number of fixations, but no reports are available regarding the visual perceptual background in relation to individuals with less or more FFA during the tissue diagnostic task. Materials and methods. In medical students, including 13 men (SD = 19.4 years) and 8 women (SD = 18.1 years), FFA was determined as follows: n = 9 FFA. The entire population performed tissue diagnostic analysis of normal skin and skin with squamous cell carcinoma pathology from digital images to establish the duration and number of fixations and the total time taken for diagnosis. Results. Individuals with > FFA show significant differences in the visual perceptual background during diagnostic analysis of normal and pathological skin, which are magnified by the fixation duration and the number of fixations when the tissue diagnosis is pathological. Conclusion. Compared to those with lower FFA, medical students with greater FFA performing tissue diagnosis of pathological tissue have visual perceptual backgrounds characterized by less time spent in each fixation but with more fixations

Correlation Of Objective Assessment Of Facial Paralysis With House-Brackmann Score

This article illustrated a brief review of some objective methods in assessing facial nerve function for facial nerve paralysis which were correlated with House-rackmann Grading System (HBGS).A rigorous search of online databases such as Springer,Elsevier and IEEE was conducted from June,2015 to November,2016 to discover and analyze the previous works in facial nerve assessment methods for facial paralysis.Several domains such as facial grading system and methods used to evaluate the facial nerve function were extracted for further analysis.Different keywords were used to acquire the studies based on the desire criteria.A total of 8 articles were identified and were analyzed for inclusion in this search.In conclusion,this review has presented an initial overview for further improvements in objective facial nerve assessment which has to be correlated with subjective assessment to make it more reliable and useful in clinical practice

Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition

Facial analysis and recognition have received substential attention from researchers in biometrics, pattern recognition, and computer vision communities. They have a large number of applications, such as security, communication, and entertainment. Although a great deal of efforts has been devoted to automated face recognition systems, it still remains a challenging uncertainty problem. This is because human facial appearance has potentially of very large intra-subject variations of head pose, illumination, facial expression, occlusion due to other objects or accessories, facial hair and aging. These misleading variations may cause classifiers to degrade generalization performance

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier

BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region