scholarly journals A Facial Expression Parameterization by Elastic Surface Model

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Ken Yano ◽  
Koichi Harada
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Source Model ◽  
Target Face ◽  
Surface Model ◽  
Control Points ◽  
Elastic Surface ◽  
Data Format ◽  
Storage Size ◽  
Facial Muscles

We introduce a novel parameterization of facial expressions by using elastic surface model. The elastic surface model has been used as a deformation tool especially for nonrigid organic objects. The parameter of expressions is either retrieved from existing articulated face models or obtained indirectly by manipulating facial muscles. The obtained parameter can be applied on target face models dissimilar to the source model to create novel expressions. Due to the limited number of control points, the animation data created using the parameterization require less storage size without affecting the range of deformation it provides. The proposed method can be utilized in many ways: (1) creating a novel facial expression from scratch, (2) parameterizing existing articulation data, (3) parameterizing indirectly by muscle construction, and (4) providing a new animation data format which requires less storage.

Facial Expression in Primates With Remarks On a Parallel Development in Certain Carnivores (a Preliminary Report On Work in Progress)

Behaviour ◽  
1964 ◽  
Vol 22 (3-4) ◽  
pp. 167-192 ◽  
Author(s):  
Niels Bolwig
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Preliminary Report ◽  
Common Ancestry ◽  
Upper Lip ◽  
Common Pattern ◽  
Facial Muscles ◽  
Facial Musculature ◽  
The Face ◽  
Emotional Condition

AbstractIn this report of an unfinished study of the evolution of facial expressions the author draws a brief comparison between the most important facial muscles of various primates and of two carnivores, the suricate and the dog. Before discussing the expressions, definitions of the various elementary emotions are given and the criteria from which the author judges the emotional condition of the animals. The main conclusions reached from the observations are:- 1. Certain basic rules govern the facial expressions of the animals studied. 2. Joy and happiness are expressed by a general lifting of the face and a tightening of the upper lip. The expression originates from preparation for a play-bite. The posture has become completely ritualised in man. 3. Unhappiness expresses itself by a lowering of the face. In horror there is a general tension of the facial muscles and the mouth tends to open while the animal screams. In sadness the animal tends to become less active. 4. Anger is recognisable from a tightening of the facial muscles, particularly those around the mouth in preparation for a hard bite. 5. Threat varies in expression but it contains components of anger and fear. 6. Love and affection find expression through such actions as lipsmacking, love-biting, sucking and kissing. The oral caressing has its origin in the juvenile sucking for comfort. 7. Concentration is not an emotion but it usually shows itself by a tension of the facial muscles. 8. There is a similarity between the two carnivores under discussion and some of the primates. A common pattern of the facial muscles of the suricate and the lemur indicate a common ancestry and brings the two animals to the same level in their ability to express their emotions. The dog, although very different from the monkey in its facial musculature nevertheless resembles it in its mode of expression. This feature seems related to similarities in their biology which have been facilitated by the development of a bifocal vision.

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 the Mechanical Head of Advanced Intelligent Robot

2014 ◽  
Vol 513-517 ◽  
pp. 4043-4046
Author(s):  
Ji Zheng Yan ◽  
Zhi Liang Wang ◽  
Yan Yan
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expressions ◽  
Humanoid Robot ◽  
Control Points ◽  
Intelligent Robot ◽  
Coding System ◽  
Human Beings ◽  
Intelligent Robots ◽  
Robot Head

Whether industrial or civilian, advanced intelligent robots are the focus of Artificial Intelligence (AI), especially which have humanoid emotion and could show anthropomorphic facial expressions, so our research focuses on how to design a humanoid robot head to show emotion to human beings. In this paper, we successively discuss three issues. Issue 1: what are the approaches and theories to make robot have humanoid emotion? Issue 2: how robot to show anthropomorphic facial expressions? Issue 3: what is the mechanical structure of the robot head? To issue 1, through analysis and comparison we choose Artificial Psychology as the means and guidance; To issue 2, we study Facial Coding System (FACS) and make innovative use, further optimize the combination of control points to construct facial expression; To issue 3, we divide the head into four parts, and each part could be driven by servos. Finally, we make a robot head according to the previous concept and design. Through experiments and correction, we achieve the expected goals of advanced intelligent robots.

Revisiting the Embryology of the Facial Muscles, the Superficial Musculoaponeurotic System, and the Facial Nerve

Neurographics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 200-228
Author(s):  
P.M. Som ◽  
P.J. Taub ◽  
B.N. Delman
Keyword(s):  
Mr Imaging ◽  
Facial Expression ◽  
Facial Nerve ◽  
Facial Expressions ◽  
Emotional Expression ◽  
Facial Muscles ◽  
Nonverbal Expression ◽  
Superficial Musculoaponeurotic System

The facial muscles are responsible for nonverbal expression, and the manner by which these muscles function to express various emotions are reviewed. How one recognizes these various facial expressions and how individuals can alter their facial expression are discussed. The methodology for cataloging facial expressions is also presented. The embryology of the facial muscles; the facial ligaments; and the supporting superficial musculoaponeurotic system, which magnifies the muscle movements, is also reviewed as is the embryology of the facial nerve, which innervates these muscles. Also, a detailed MR imaging atlas of the facial muscles is presented.Learning Objective: The reader will learn how the facial muscles develop and how they are the means of human nonverbal emotional expression. The anatomy of the facial ligaments and the superficial musculoaponeurotic system are also discussed

Perception of Photographic-Quality Caricatures of Emotional Facial Expressions

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 28-28
Author(s):  
A J Calder ◽  
A W Young ◽  
D Rowland ◽  
D R Gibbenson ◽  
B M Hayes ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Mental Representation ◽  
Reaction Times ◽  
Target Face ◽  
Emotional Expressions ◽  
Emotional Facial Expressions ◽  
Basic Emotions ◽  
Happy Expression ◽  
Neutral Expression

G Rhodes, S E Brennan, S Carey (1987 Cognitive Psychology19 473 – 497) and P J Benson and D I Perrett (1991 European Journal of Cognitive Psychology3 105 – 135) have shown that computer-enhanced (caricatured) representations of familiar faces are named faster and rated as better likenesses than veridical (undistorted) representations. Here we have applied Benson and Perrett's graphic technique to examine subjects' perception of enhanced representations of photographic-quality facial expressions of basic emotions. To enhance a facial expression the target face is compared to a norm or prototype face, and, by exaggerating the differences between the two, a caricatured image is produced; reducing the differences results in an anticaricatured image. In experiment 1 we examined the effect of degree of caricature and types of norm on subjects' ratings for ‘intensity of expression’. Three facial expressions (fear, anger, and sadness) were caricatured at seven levels (−50%, −30%, −15%, 0%, +15%, +30%, and +50%) relative to three different norms; (1) an average norm prepared by blending pictures of six different emotional expressions; (2) a neutral expression norm; and (3) a different expression norm (eg anger caricatured relative to a happy expression). Irrespective of norm, the caricatured expressions were rated as significantly more intense than the veridical images. Furthermore, for the average and neutral norm sets, the anticaricatures were rated as significantly less intense. We also examined subjects' reaction times to recognise caricatured (−50%, 0%, and +50%) representations of six emotional facial expressions. The results showed that the caricatured images were identified fastest, followed by the veridical, and then anticaricatured images. Hence the perception of facial expression and identity is facilitated by caricaturing; this has important implications for the mental representation of facial expressions.

The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

2020 ◽  
Author(s):  
Jonathan Yi ◽  
Philip Pärnamets ◽  
Andreas Olsson
Keyword(s):  
Decision Making ◽  
Facial Expression ◽  
Facial Expressions ◽  
Large Body ◽  
Mechanistic Explanation ◽  
Experimental Manipulation ◽  
Corrugator Supercilii ◽  
Dynamic Faces ◽  
Facial Behavior ◽  
Reinforcement Learning Models

Responding appropriately to others’ facial expressions is key to successful social functioning. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how goal directed responses to faces are influenced by learning during dyadic interactions. To experimentally model such situations, we developed a novel method based on online integration of electromyography (EMG) signals from the participants’ face (corrugator supercilii and zygomaticus major) during facial expression exchange with dynamic faces displaying happy and angry facial expressions. Fifty-eight participants learned by trial-and-error to avoid receiving aversive stimulation by either reciprocate (congruently) or respond opposite (incongruently) to the expression of the target face. Our results validated our method, showing that participants learned to optimize their facial behavior, and replicated earlier findings of faster and more accurate responses in congruent vs. incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, as compared to frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Finally, we applied drift diffusion and reinforcement learning models to provide a mechanistic explanation for our findings which helped clarifying the underlying decision-making processes of our experimental manipulation. Our results introduce a new method to study learning and decision-making in facial expression exchange, in which there is a need to gradually adapt facial expression selection to both social and non-social reinforcements.

Capacity-Free Automatic Processing of Facial Expressions of Emotion

2020 ◽  
Author(s):  
Joshua W Maxwell ◽  
Eric Ruthruff ◽  
michael joseph
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Processing ◽  
Emotional Expressions ◽  
Facial Expression Of Emotion ◽  
Expression Of Emotion ◽  
Facial Expressions Of Emotion ◽  
Correspondence Effect ◽  
Experimental Logic ◽  
Face Task

Are facial expressions of emotion processed automatically? Some authors have not found this to be the case (Tomasik et al., 2009). Here we revisited the question with a novel experimental logic – the backward correspondence effect (BCE). In three dual-task studies, participants first categorized a sound (Task 1) and then indicated the location of a target face (Task 2). In Experiment 1, Task 2 required participants to search for one facial expression of emotion (angry or happy). We observed positive BCEs, indicating that facial expressions of emotion bypassed the central attentional bottleneck and thus were processed in a capacity-free, automatic manner. In Experiment 2, we replicated this effect but found that morphed emotional expressions (which were used by Tomasik) were not processed automatically. In Experiment 3, we observed similar BCEs for another type of face processing previously shown to be capacity-free – identification of familiar faces (Jung et al., 2013). We conclude that facial expressions of emotion are identified automatically when sufficiently unambiguous.

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

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