Facial Asymmetry Analysis Based on 3-D Dynamic Scans

Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients

Frontal Facial Symmetry Detection Using Eigenvalue Method

Facial symmetry is correspondence of face components on the both sides of face, left and right of a dividing line or about a center or an axis. Most of the research use face component like eyes, nose and ears component to identify facial symmetry. In this paper we suggest to add mouth as another face component to increase accuracy in facial symmetry detection. The results of facial symmetry detection are used for authentication process, analysis in medical, psychology and anthropology scope. By using MATLAB 7.1 we develop a program that can analyze face,asymmetry or not with utilizing eigenvalue. The contribution of this analysis is to know whether eigenvalue is suitable or not in analyzing facial symmetry

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

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

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.

Quantification of Facial Traits

Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability

Cognitive representation of facial asymmetry

The human face displays mild asymmetry, with measurements of facial structure differing from left to right of the meridian by an average of three percent. Presently this source of variation is of theoretical interest primarily to researchers studying the perception of beauty, but a very limited amount of research has addressed the question of how this variation contributes to the cognitive processes underlying face recognition. This is surprising given that measurement of facial asymmetry can reliably distinguish between even the most similar of faces. Furthermore, brain regions responsible for symmetry detection support face-processing regions, and detection of symmetry is superior in upright faces relative to inverted and contrast-reversed face stimuli. In addition, facial asymmetry provides a useful biometric for automatic face recognition systems, and understanding the contribution of facial asymmetry in human face recognition may therefore inform the development of these systems. In this thesis the extent to which facial asymmetry is implicated in the process of recognition in human participants is quantified. By measuring the effect of left-right reversal on various tasks of face processing, the degree to which facial asymmetry is represented by memory is investigated. Marginal sensitivity to mirror reversal is demonstrated in a number of instances, and it is therefore concluded that cognitive representations of faces specify structural asymmetry. Reversal effects are typically slight however and on a number of occasions no reliable effect of this stimulus manipulation is detected. It is likely that a general tendency to treat mirror reversals as equivalent stimuli, in addition to an inability to recall lateral orientation of objects from memory, somewhat obscure the effect of reversal. The findings are discussed in the context of existing literature examining the way in which faces are cognitively represented