scholarly journals Facial Action Units Analysis using Rule-Based Algorithm

2018 ◽  
Vol 7 (3.20) ◽  
pp. 284
Author(s):  
Hamimah Ujir ◽  
Irwandi Hipiny ◽  
D N.F. Awang Iskandar
Keyword(s):  
Facial Expressions ◽  
Facial Features ◽  
Coding System ◽  
Rule Based ◽  
Facial Action ◽  
Action Units ◽  
Offset Time ◽  
Rule Based Approach ◽  
Happy Expression ◽  
Temporal Segment

Most works in quantifying facial deformation are based on action units (AUs) provided by the Facial Action Coding System (FACS) which describes facial expressions in terms of forty-six component movements. AU corresponds to the movements of individual facial muscles. This paper presents a rule based approach to classify the AU which depends on certain facial features. This work only covers deformation of facial features based on posed Happy and the Sad expression obtained from the BU-4DFE database. Different studies refer to different combination of AUs that form Happy and Sad expression. According to the FACS rules lined in this work, an AU has more than one facial property that need to be observed. The intensity comparison and analysis on the AUs involved in Sad and Happy expression are presented. Additionally, dynamic analysis for AUs is studied to determine the temporal segment of expressions, i.e. duration of onset, apex and offset time. Our findings show that AU15, for sad expression, and AU12, for happy expression, show facial features deformation consistency for all properties during the expression period. However for AU1 and AU4, their properties’ intensity is different during the expression period. 

Detection of confusion behavior using a facial expression based on different classification algorithms

2021 ◽  
Vol 39 (2A) ◽  
pp. 316-325
Author(s):  
Fatima I. Yasser ◽  
Bassam H. Abd ◽  
Saad M. Abbas
Keyword(s):  
Logistic Regression ◽  
Facial Expressions ◽  
Cost Effective ◽  
Facial Features ◽  
Classification Algorithms ◽  
Facial Muscle ◽  
Coding System ◽  
Facial Action Coding System ◽  
Facial Action ◽  
Detection Systems

Confusion detection systems (CDSs) that need Noninvasive, mobile, and cost-effective methods use facial expressions as a technique to detect confusion. In previous works, the technology that the system used represents a major gap between this proposed CDS and other systems. This CDS depends on the Facial Action Coding System (FACS) that is used to extract facial features. The FACS shows the motion of the facial muscles represented by Action Units (AUs); the movement is represented with one facial muscle or more. Seven AUs are used as possible markers for detecting confusion that has been implemented in the form of a single vector of facial action; the AUs that have been used in this work are AUs 4, 5, 6, 7, 10, 12, and 23. The database used to calculate the performance of the proposed CDS is gathered from 120 participants (91males, 29 females), between the ages of 18-45. Four types of classification algorithms are used as individuals; these classifiers are (VG-RAM), (SVM), Logistic Regression and Quadratic Discriminant classifiers. The best success rate was found when using Logistic Regression and Quadratic Discriminant. This work introduces different classification techniques to detect confusion by collecting an actual database that can be used to evaluate the performance for every CDS employing facial expressions and selecting appropriate facial features.

scholarly journals FACSGen: A Tool to Synthesize Emotional Facial Expressions Through Systematic Manipulation of Facial Action Units

2010 ◽  
Vol 35 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Etienne B. Roesch ◽  
Lucas Tamarit ◽  
Lionel Reveret ◽  
Didier Grandjean ◽  
David Sander ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Emotional Facial Expressions ◽  
Facial Action ◽  
Action Units ◽  
Systematic Manipulation

scholarly journals Facial movements have over twenty dimensions of perceived meaning that are only partially captured with traditional methods

2021 ◽  
Author(s):  
Alan S. Cowen ◽  
Kunalan Manokara ◽  
Xia Fang ◽  
Disa Sauter ◽  
Jeffrey A Brooks ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Gold Standard ◽  
Facial Muscle ◽  
Coding System ◽  
Free Response ◽  
Facial Action ◽  
Facial Movements ◽  
Wide Range ◽  
Emotion Concepts ◽  
Perceived Emotion

Central to science and technology are questions about how to measure facial expression. The current gold standard is the facial action coding system (FACS), which is often assumed to account for all facial muscle movements relevant to perceived emotion. However, the mapping from FACS codes to perceived emotion is not well understood. Six prototypical configurations of facial action units (AU) are sometimes assumed to account for perceived emotion, but this hypothesis remains largely untested. Here, using statistical modeling, we examine how FACS codes actually correspond to perceived emotions in a wide range of naturalistic expressions. Each of 1456 facial expressions was independently FACS coded by two experts (r = .84, κ = .84). Naive observers reported the emotions they perceived in each expression in many different ways, including emotions (N = 666); valence, arousal and appraisal dimensions (N =1116); authenticity (N = 121), and free response (N = 193). We find that facial expressions are much richer in meaning than typically assumed: At least 20 patterns of facial muscle movements captured by FACS have distinct perceived emotional meanings. Surprisingly, however, FACS codes do not offer a complete description of real-world facial expressions, capturing no more than half of the reliable variance in perceived emotion. Our findings suggest that the perceived emotional meanings of facial expressions are most accurately and efficiently represented using a wide range of carefully selected emotion concepts, such as the Cowen & Keltner (2019) taxonomy of 28 emotions. Further work is needed to characterize the anatomical bases of these facial expressions.

scholarly journals 3D Facial Action Units Recognition for Emotional Expression

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1317-1323
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Emotional Expression ◽  
Support Vector ◽  
Human Face ◽  
Classification Result ◽  
Facial Action ◽  
Action Units ◽  
Happy Expression ◽  
Incorrect Classification

The muscular activities caused the activation of facial action units (AUs) when a facial expression is shown by a human face. This paper presents the methods to recognize AU using a distance feature between facial points which activates the muscles. The seven AU involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterizes a happy and sad expression. The recognition is performed on each AU according to the rules defined based on the distance of each facial point. The facial distances chosen are computed from twelve salient facial points. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase. By using any SVM kernels, it is consistent that AUs that are corresponded to sad expression has a high recognition compared to happy expression. The highest average kernel performance across AUs is 93%, scored by quadratic kernel. Best results for NN across AUs is for AU25 (Lips parted) with lowest CE (0.38%) and 0% incorrect classification.

Facial Action Recognition in 2D and 3D

2014 ◽  
pp. 167-186 ◽  
Author(s):  
Michel Valstar ◽  
Stefanos Zafeiriou ◽  
Maja Pantic
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Analysis ◽  
Coding System ◽  
Recording Equipment ◽  
Facial Action ◽  
Facial Expression Analysis ◽  
Challenges And Opportunities ◽  
Almost All ◽  
2D And 3D

Automatic Facial Expression Analysis systems have come a long way since the earliest approaches in the early 1970s. We are now at a point where the first systems are commercially applied, most notably smile detectors included in digital cameras. As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has received significant and sustained attention within the field. Over the past 30 years, psychologists and neuroscientists have conducted extensive research on various aspects of human behaviour using facial expression analysis coded in terms of FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Mainly due to the cost effectiveness of existing recording equipment, until recently almost all work conducted in this area involves 2D imagery, despite their inherent problems relating to pose and illumination variations. In order to deal with these problems, 3D recordings are increasingly used in expression analysis research. In this chapter, the authors give an overview of 2D and 3D FACS recognition, and summarise current challenges and opportunities.

scholarly journals Objective Classes for Micro-Facial Expression Recognition

2018 ◽  
Vol 4 (10) ◽  
pp. 119 ◽  
Author(s):  
Adrian Davison ◽  
Walied Merghani ◽  
Moi Yap
Keyword(s):  
Facial Expressions ◽  
Local Binary Patterns ◽  
Expression Recognition ◽  
Coding System ◽  
Histogram Of Oriented Gradient ◽  
Action Units ◽  
Self Reports ◽  
Casme Ii ◽  
Academy Of Sciences ◽  
Micro Expression

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset (Chinese Academy of Sciences Micro-expression II) are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP (Local Binary Patterns from Three Orthogonal Planes), HOOF (Histograms of Oriented Optical Flow) and HOG 3D (3D Histogram of Oriented Gradient) feature descriptors. The experiments are evaluated on two benchmark FACS (Facial Action Coding System) coded datasets: CASME II and SAMM (A Spontaneous Micro-Facial Movement). The best result achieves 86.35% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.

Facial Expressions of Patients With Dementia: A Comparison of Two Methods of Interpretation

1995 ◽  
Vol 7 (4) ◽  
pp. 527-534 ◽  
Author(s):  
Kenneth Asplund ◽  
Lilian Jansson ◽  
Astrid Norberg
Keyword(s):  
Facial Expressions ◽  
Coding System ◽  
Facial Action Coding System ◽  
Alzheimer Type ◽  
Severe Dementia ◽  
Facial Action ◽  
Facial Action Coding

Two methods of interpreting the videotaped facial expressions of four patients with severe dementia of the Alzheimer type were compared. Interpretations of facial expressions performed by means of unstructured naturalistic judgements revealed episodes when the four patients exhibited anger, disgust, happiness, sadness, and surprise. When these episodes were assessed by use of modified version of the Facial Action Coding System, there was, in total, 48% agreement between the two methods. The highest agreement, 98%, occurred for happiness shown by one patient. It was concluded that more emotions could be judged by means of the unstructured naturalistic method, which is based on an awareness of the total situation that facilitates imputing meaning into the patients' cues. It is a difficult task to find a balance between imputing too much meaning into the severely demented patients' sparse and unclear cues and ignoring the possibility that there is some meaning to be interpreted.

scholarly journals 53 Computer Vision, Facial Expressivity and Schizophrenia: A Review

CNS Spectrums ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 204-205
Author(s):  
Mina Boazak ◽  
Robert Cotes
Keyword(s):  
Computer Vision ◽  
Facial Expressions ◽  
Predictive Accuracy ◽  
Coding System ◽  
Facial Action Coding System ◽  
Action Unit ◽  
Facial Action ◽  
The Past ◽  
Facial Expressivity ◽  
Facial Action Coding

AbstractIntroductionFacial expressivity in schizophrenia has been a topic of clinical interest for the past century. Besides the schizophrenia sufferers difficulty decoding the facial expressions of others, they often have difficulty encoding facial expressions. Traditionally, evaluations of facial expressions have been conducted by trained human observers using the facial action coding system. The process was slow and subject to intra and inter-observer variability. In the past decade the traditional facial action coding system developed by Ekman has been adapted for use in affective computing. Here we assess the applications of this adaptation for schizophrenia, the findings of current groups, and the future role of this technology.Materials and MethodsWe review the applications of computer vision technology in schizophrenia using pubmed and google scholar search criteria of “computer vision” AND “Schizophrenia” from January of 2010 to June of 2018.ResultsFive articles were selected for inclusion representing 1 case series and 4 case-control analysis. Authors assessed variations in facial action unit presence, intensity, various measures of length of activation, action unit clustering, congruence, and appropriateness. Findings point to variations in each of these areas, except action unit appropriateness, between control and schizophrenia patients. Computer vision techniques were also demonstrated to have high accuracy in classifying schizophrenia from control patients, reaching an AUC just under 0.9 in one study, and to predict psychometric scores, reaching pearson’s correlation values of under 0.7.DiscussionOur review of the literature demonstrates agreement in findings of traditional and contemporary assessment techniques of facial expressivity in schizophrenia. Our findings also demonstrate that current computer vision techniques have achieved capacity to differentiate schizophrenia from control populations and to predict psychometric scores. Nevertheless, the predictive accuracy of these technologies leaves room for growth. On analysis our group found two modifiable areas that may contribute to improving algorithm accuracy: assessment protocol and feature inclusion. Based on our review we recommend assessment of facial expressivity during a period of silence in addition to an assessment during a clinically structured interview utilizing emotionally evocative questions. Furthermore, where underfit is a problem we recommend progressive inclusion of features including action unit activation, intensity, action unit rate of onset and offset, clustering (including richness, distribution, and typicality), and congruence. Inclusion of each of these features may improve algorithm predictive accuracy.ConclusionWe review current applications of computer vision in the assessment of facial expressions in schizophrenia. We present the results of current innovative works in the field and discuss areas for continued development.

scholarly journals Recognition of Tactile Facial Action Units by Individuals Who Are Blind and Sighted: A Comparative Study

2019 ◽  
Vol 3 (2) ◽  
pp. 32 ◽  
Author(s):  
Troy McDaniel ◽  
Diep Tran ◽  
Abhik Chowdhury ◽  
Bijan Fakhri ◽  
Sethuraman Panchanathan
Keyword(s):  
Social Interactions ◽  
Facial Expressions ◽  
Building Blocks ◽  
Body Language ◽  
Pilot Test ◽  
Absolute Identification ◽  
Sensory Augmentation ◽  
Facial Action ◽  
Action Units ◽  
Study Results

Given that most cues exchanged during a social interaction are nonverbal (e.g., facial expressions, hand gestures, body language), individuals who are blind are at a social disadvantage compared to their sighted peers. Very little work has explored sensory augmentation in the context of social assistive aids for individuals who are blind. The purpose of this study is to explore the following questions related to visual-to-vibrotactile mapping of facial action units (the building blocks of facial expressions): (1) How well can individuals who are blind recognize tactile facial action units compared to those who are sighted? (2) How well can individuals who are blind recognize emotions from tactile facial action units compared to those who are sighted? These questions are explored in a preliminary pilot test using absolute identification tasks in which participants learn and recognize vibrotactile stimulations presented through the Haptic Chair, a custom vibrotactile display embedded on the back of a chair. Study results show that individuals who are blind are able to recognize tactile facial action units as well as those who are sighted. These results hint at the potential for tactile facial action units to augment and expand access to social interactions for individuals who are blind.

scholarly journals Extending the MaqFACS to measure facial movement in Japanese macaques (Macaca fuscata) reveals a wide repertoire potential

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245117
Author(s):  
Catia Correia-Caeiro ◽  
Kathryn Holmes ◽  
Takako Miyabe-Nishiwaki
Keyword(s):  
Facial Expressions ◽  
Rhesus Macaques ◽  
Japanese Macaques ◽  
Primate Species ◽  
Coding System ◽  
Facial Movement ◽  
Action Units ◽  
Facial Musculature ◽  
In Captivity ◽  
Evolution Of Communication

Facial expressions are complex and subtle signals, central for communication and emotion in social mammals. Traditionally, facial expressions have been classified as a whole, disregarding small but relevant differences in displays. Even with the same morphological configuration different information can be conveyed depending on the species. Due to a hardwired processing of faces in the human brain, humans are quick to attribute emotion, but have difficulty in registering facial movement units. The well-known human FACS (Facial Action Coding System) is the gold standard for objectively measuring facial expressions, and can be adapted through anatomical investigation and functional homologies for cross-species systematic comparisons. Here we aimed at developing a FACS for Japanese macaques, following established FACS methodology: first, we considered the species’ muscular facial plan; second, we ascertained functional homologies with other primate species; and finally, we categorised each independent facial movement into Action Units (AUs). Due to similarities in the rhesus and Japanese macaques’ facial musculature, the MaqFACS (previously developed for rhesus macaques) was used as a basis to extend the FACS tool to Japanese macaques, while highlighting the morphological and appearance changes differences between the two species. We documented 19 AUs, 15 Action Descriptors (ADs) and 3 Ear Action Units (EAUs) in Japanese macaques, with all movements of MaqFACS found in Japanese macaques. New movements were also observed, indicating a slightly larger repertoire than in rhesus or Barbary macaques. Our work reported here of the MaqFACS extension for Japanese macaques, when used together with the MaqFACS, comprises a valuable objective tool for the systematic and standardised analysis of facial expressions in Japanese macaques. The MaqFACS extension for Japanese macaques will now allow the investigation of the evolution of communication and emotion in primates, as well as contribute to improving the welfare of individuals, particularly in captivity and laboratory settings.

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