scholarly journals Realistic Facial Expression of Virtual Human Based on Color, Sweat, and Tears Effects

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mohammed Hazim Alkawaz ◽  
Ahmad Hoirul Basori ◽  
Dzulkifli Mohamad ◽  
Farhan Mohamed
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Skin Color ◽  
Particle System ◽  
Facial Animation ◽  
Virtual Human ◽  
Coding System ◽  
Animation Industry ◽  
Particle Hydrodynamics ◽  
Fluid Properties

Generating extreme appearances such as scared awaiting sweating while happy fit for tears (cry) and blushing (anger and happiness) is the key issue in achieving the high quality facial animation. The effects of sweat, tears, and colors are integrated into a single animation model to create realistic facial expressions of 3D avatar. The physical properties of muscles, emotions, or the fluid properties with sweating and tears initiators are incorporated. The action units (AUs) of facial action coding system are merged with autonomous AUs to create expressions including sadness, anger with blushing, happiness with blushing, and fear. Fluid effects such as sweat and tears are simulated using the particle system and smoothed-particle hydrodynamics (SPH) methods which are combined with facial animation technique to produce complex facial expressions. The effects of oxygenation of the facial skin color appearance are measured using the pulse oximeter system and the 3D skin analyzer. The result shows that virtual human facial expression is enhanced by mimicking actual sweating and tears simulations for all extreme expressions. The proposed method has contribution towards the development of facial animation industry and game as well as computer graphics.

Spontaneous Facial Expression Analysis and Synthesis for Interactive Facial Animation

2011 ◽  
pp. 20-37
Author(s):  
Yongmian Zhang ◽  
Jixu Chen ◽  
Yan Tong ◽  
Qiang Ji
Keyword(s):  
Facial Expression ◽  
Bayesian Network ◽  
Facial Expressions ◽  
Expression Analysis ◽  
Facial Animation ◽  
Head Movements ◽  
Perceptual Quality ◽  
Facial Action ◽  
Facial Expression Analysis ◽  
Analysis And Synthesis

This chapter describes a probabilistic framework for faithful reproduction of spontaneous facial expressions on a synthetic face model in a real time interactive application. The framework consists of a coupled Bayesian network (BN) to unify the facial expression analysis and synthesis into one coherent structure. At the analysis end, we cast the facial action coding system (FACS) into a dynamic Bayesian network (DBN) to capture relationships between facial expressions and the facial motions as well as their uncertainties and dynamics. The observations fed into the DBN facial expression model are measurements of facial action units (AUs) generated by an AU model. Also implemented by a DBN, the AU model captures the rigid head movements and nonrigid facial muscular movements of a spontaneous facial expression. At the synthesizer, a static BN reconstructs the Facial Animation Parameters (FAPs) and their intensity through the top-down inference according to the current state of facial expression and pose information output by the analysis end. The two BNs are connected statically through a data stream link. The novelty of using the coupled BN brings about several benefits. First, a facial expression is inferred through both spatial and temporal inference so that the perceptual quality of animation is less affected by the misdetection of facial features. Second, more realistic looking facial expressions can be reproduced by modeling the dynamics of human expressions in facial expression analysis. Third, very low bitrate (9 bytes per frame) in data transmission can be achieved.

Facial Expression Recognition

2011 ◽  
pp. 255-317 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Muscle Group ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Multiple Cultures ◽  
Expression Of Emotions ◽  
Objective Description

The facial expression has long been an interest for psychology, since Darwin published The expression of Emotions in Man and Animals (Darwin, C., 1899). Psychologists have studied to reveal the role and mechanism of the facial expression. One of the great discoveries of Darwin is that there exist prototypical facial expressions across multiple cultures on the earth, which provided the theoretical backgrounds for the vision researchers who tried to classify categories of the prototypical facial expressions from images. The representative 6 facial expressions are afraid, happy, sad, surprised, angry, and disgust (Mase, 1991; Yacoob and Davis, 1994). On the other hand, real facial expressions that we frequently meet in daily life consist of lots of distinct signals, which are subtly different. Further research on facial expressions required an object method to describe and measure the distinct activity of facial muscles. The facial action coding system (FACS), proposed by Hager and Ekman (1978), defines 46 distinct action units (AUs), each of which explains the activity of each distinct muscle or muscle group. The development of the objective description method also affected the vision researchers, who tried to detect the emergence of each AU (Tian et. al., 2001).

scholarly journals Facial Expression Recognition Using Fast Walidlet Hybrid Transform

2020 ◽  
pp. 59-69
Author(s):  
Walid Mahmod ◽  
Jane Stephan ◽  
Anmar Razzak
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Good Accuracy ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Artificial Intelligence Research ◽  
Multiwavelet Transform

Automatic analysis of facial expressions is rapidly becoming an area of intense interest in computer vision and artificial intelligence research communities. In this paper an approach is presented for facial expression recognition of the six basic prototype expressions (i.e., joy, surprise, anger, sadness, fear, and disgust) based on Facial Action Coding System (FACS). The approach is attempting to utilize a combination of different transforms (Walid let hybrid transform); they consist of Fast Fourier Transform; Radon transform and Multiwavelet transform for the feature extraction. Korhonen Self Organizing Feature Map (SOFM) then used for patterns clustering based on the features obtained from the hybrid transform above. The result shows that the method has very good accuracy in facial expression recognition. However, the proposed method has many promising features that make it interesting. The approach provides a new method of feature extraction in which overcome the problem of the illumination, faces that varies from one individual to another quite considerably due to different age, ethnicity, gender and cosmetic also it does not require a precise normalization and lighting equalization. An average clustering accuracy of 94.8% is achieved for six basic expressions, where different databases had been used for the test of the method.

scholarly journals Adaptive 3D Model-Based Facial Expression Synthesis and Pose Frontalization

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2578
Author(s):  
Yu-Jin Hong ◽  
Sung Eun Choi ◽  
Gi Pyo Nam ◽  
Heeseung Choi ◽  
Junghyun Cho ◽  
...  
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Facial Expressions ◽  
Data Augmentation ◽  
Synthesis Method ◽  
Qualitative Evaluation ◽  
Emotional States ◽  
Coding System ◽  
Contour Fitting ◽  
Facial Expression Modeling

Facial expressions are one of the important non-verbal ways used to understand human emotions during communication. Thus, acquiring and reproducing facial expressions is helpful in analyzing human emotional states. However, owing to complex and subtle facial muscle movements, facial expression modeling from images with face poses is difficult to achieve. To handle this issue, we present a method for acquiring facial expressions from a non-frontal single photograph using a 3D-aided approach. In addition, we propose a contour-fitting method that improves the modeling accuracy by automatically rearranging 3D contour landmarks corresponding to fixed 2D image landmarks. The acquired facial expression input can be parametrically manipulated to create various facial expressions through a blendshape or expression transfer based on the FACS (Facial Action Coding System). To achieve a realistic facial expression synthesis, we propose an exemplar-texture wrinkle synthesis method that extracts and synthesizes appropriate expression wrinkles according to the target expression. To do so, we constructed a wrinkle table of various facial expressions from 400 people. As one of the applications, we proved that the expression-pose synthesis method is suitable for expression-invariant face recognition through a quantitative evaluation, and showed the effectiveness based on a qualitative evaluation. We expect our system to be a benefit to various fields such as face recognition, HCI, and data augmentation for deep learning.

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 Implications for Emotion: Using Anatomically Based Facial Coding to Compare Emoji Faces Across Platforms

2021 ◽  
Vol 12 ◽  
Author(s):  
Jennifer M. B. Fugate ◽  
Courtny L. Franco
Keyword(s):  
Individual Differences ◽  
Facial Expression ◽  
Facial Expressions ◽  
Current Paper ◽  
Emotional Communication ◽  
Coding System ◽  
Facial Action Coding System ◽  
Human Faces ◽  
Everyday Communication ◽  
Human Facial Expression

Emoji faces, which are ubiquitous in our everyday communication, are thought to resemble human faces and aid emotional communication. Yet, few studies examine whether emojis are perceived as a particular emotion and whether that perception changes based on rendering differences across electronic platforms. The current paper draws upon emotion theory to evaluate whether emoji faces depict anatomical differences that are proposed to differentiate human depictions of emotion (hereafter, “facial expressions”). We modified the existing Facial Action Coding System (FACS) (Ekman and Rosenberg, 1997) to apply to emoji faces. An equivalent “emoji FACS” rubric allowed us to evaluate two important questions: First, Anatomically, does the same emoji face “look” the same across platforms and versions? Second, Do emoji faces perceived as a particular emotion category resemble the proposed human facial expression for that emotion? To answer these questions, we compared the anatomically based codes for 31 emoji faces across three platforms and two version updates. We then compared those codes to the proposed human facial expression prototype for the emotion perceived within the emoji face. Overall, emoji faces across platforms and versions were not anatomically equivalent. Moreover, the majority of emoji faces did not conform to human facial expressions for an emotion, although the basic anatomical codes were shared among human and emoji faces. Some emotion categories were better predicted by the assortment of anatomical codes than others, with some individual differences among platforms. We discuss theories of emotion that help explain how emoji faces are perceived as an emotion, even when anatomical differences are not always consistent or specific to an emotion.

scholarly journals Changes in Facial Recognition and Facial Expressions with Age

2021 ◽  
Author(s):  
Hyunwoong Ko ◽  
Kisun Kim ◽  
Minju Bae ◽  
Myo-Geong Seo ◽  
Gieun Nam ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Task ◽  
The Elderly ◽  
Coding System ◽  
Facial Action Coding System ◽  
Top Down ◽  
Lower Face ◽  
Better Than

The ability to express and recognize emotion via facial expressions is well known to change with age. The present study investigated the differences in the facial recognition and facial expression of the elderly (n = 57) and the young (n = 115) and measure how each group uses different facial muscles for each emotion with Facial Action Coding System (FACS). In facial recognition task, the elderly did not recognize facial expressions better than young people and reported stronger feelings of fear and sad from photographs. In making facial expression task, the elderly rated all their facial expressions as stronger than the younger, but in fact, they expressed strong expressions in fear and anger. Furthermore, the elderly used more muscles in the lower face when making facial expressions than younger people. These results help to understand better how the facial recognition and expression of the elderly change, and show that the elderly do not effectively execute the top-down processing concerning facial expression.

Judging and coding facial expression of emotions in congenitally blind children

2001 ◽  
Vol 25 (3) ◽  
pp. 268-278 ◽  
Author(s):  
Dario Galati ◽  
Renato Miceli ◽  
Barbara Sini
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Control Group ◽  
Two Dimensional ◽  
Coding System ◽  
Facial Movement ◽  
Blind Children ◽  
Congenitally Blind ◽  
Expression Of Emotions ◽  
Facial Expressiveness

We investigate the facial expression of emotions in very young congenitally blind children to ” nd out whether these are objectively and subjectively recognisable. We also try to see whether the adequacy of the facial expression of emotions changes as the children get older. We video recorded the facial expressions of 10 congenitally blind children and 10 sighted children (as a control group) in seven everyday situations considered as emotion elicitors. The recorded sequences were analysed according to the Maximally Discriminative Facial Movement Coding System (Max; Izard, 1979) and then judged by 280 decoders who used four scales (two dimensional and two categorical) for their answers. The results showed that all the subjects (both the blind and the sighted) were able to express their emotions facially, though not always according to the theoretically expected pattern. Recognition of the various expressions was fairly accurate, but some emotions were systematically confused with others. The decoders’ answers to the dimensional and categorical scales were similar for both blind and sighted subjects. Our ” ndings on objective and subjective judgements show that there was no decrease in the facial expressiveness of the blind children in the period of development considered.

scholarly journals GAMBARAN MUSCULI FACIALIS PADA EKSPRESI WAJAH DAN EMOSI DENGAN MENGGUNAKAN FACIAL ACTION CODING SYSTEM PADA CALON PRESIDEN JOKOWI

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Friska G. Batoteng ◽  
Taufiq F. Pasiak ◽  
Shane H. R. Ticoalu
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Orbicularis Oculi ◽  
Facial Action ◽  
Facial Muscles ◽  
The Face ◽  
Facial Action Coding

Abstract: Facial expression recognition is one way to recognize emotions which has not received much attention. Muscles that form facial expressions known as musculli facial, muscles that move the face and form human facial expressions: happy, sad, angry, fearful, disgusted and surprised which are the six basic expressions of human emotion. Human facial expressions can be measured using FACS (Facial Action Coding System). This study aims to determine the facial muscles which most frequently used and most rarely used, and determine the emotion expression of Jokowi, a presidential candidate, through assessment of the facial muscles using FACS. This study is a retrospective descriptive study. The research samples are the whole photo of Jokowi’s facial expression at first presidential debate in 2014, about 30 photos. Samples were taken from a video debate and confirmed to be a photo using Jokowi’s facial expressions which then further analyzed using FACS. The research showed that the most used action units and facial muscle is AU 1 whose work on frontal muscle pars medialis (14.75%). The least appear muscles on Jokowi’s facial expressions were musculus orbicularis oculi, pars palpebralis and AU 24 musculus obicularis oris (0.82%). The dominant facial expressions was seen in Jokowi was sad facial expression (36.67%).Keywords: musculi facialis, facial expression, expression of emotion, FACSAbstrak: Pengenalan ekspresi wajah adalah salah satu cara untuk mengenali emosi yang belum banyak diperhatikan. Otot-otot yang membentuk ekspresi wajah yaitu musculli facialis yang merupakan otot-otot penggerak wajah dan membentuk ekspresi – ekspresi wajah manusia yaitu bahagia, sedih, marah, takut, jijik dan terkejut yang merupakan 6 dasar ekspresi emosi manusia. Ekspresi wajah manusia dapat diukur dengan menggunakan parameter FACS (Facial Action Coding System). Penelitian ini bertujuan untuk mengetahui musculi facialis yang paling sering digunakan dan yang paling jarang digunakan, serta untuk menentukan ekspresi emosi calon presiden Jokowi. Desain penelitian ini yaitu penelitian deskriptif dengan retrospektif. Sampel penelitian ialah seluruh foto ekspresi wajah Jokowi saat debat calon presiden pertama tahun 2014 sebanyak 30 foto. Sampel diambil dari video debat dan dikonfirmasi menjadi foto kemudian dianalisis lebih lanjut menggunakan FACS. Penelitian ini didapatkan hasil bahwa Musculi yang paling banyak digerakkan, yaitu Musculi frontalis pars medialis (14,75%). Musculi yang paling sedikit muncul pada ekspresi wajah Jokowi yaitu musculus orbicularis oculi, pars palpebralis dan musculus obicularis oris (0,82%). Ekspresi wajah yang dominan dinampakkan oleh Jokowi merupakan ekspresi wajah sedih (36,67%).Kata kunci: musculi facialis, ekspresi wajah, ekspresi emosi, FACS

Research on Facial Expression Computing in Experiment Process

2015 ◽  
Vol 738-739 ◽  
pp. 666-669
Author(s):  
Yao Feng Xue ◽  
Hua Li Sun ◽  
Ye Duan
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Feature Points ◽  
Coding System ◽  
Teaching Process ◽  
Application System ◽  
Face Model ◽  
Action Units ◽  
The Face

The Candide face model and the Face Action Coding System (FACS) are introduced in the paper. The relations of the positions of feature points of Candide-3 model and the action units of FACS are studied. The application system for computing the facial expressions of students in the experiment teaching process is developed. The feasibility of the application system is demonstrated.

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