scholarly journals The Analysis of Emotion Authenticity Based on Facial Micromovements

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4616
Author(s):  
Sung Park ◽  
Seong Won Lee ◽  
Mincheol Whang
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Future Research ◽  
Monitoring Systems ◽  
Two Dimensional ◽  
Expression Data ◽  
Vibration Level ◽  
Emotion Model ◽  
Recognition Systems

People tend to display fake expressions to conceal their true feelings. False expressions are observable by facial micromovements that occur for less than a second. Systems designed to recognize facial expressions (e.g., social robots, recognition systems for the blind, monitoring systems for drivers) may better understand the user’s intent by identifying the authenticity of the expression. The present study investigated the characteristics of real and fake facial expressions of representative emotions (happiness, contentment, anger, and sadness) in a two-dimensional emotion model. Participants viewed a series of visual stimuli designed to induce real or fake emotions and were signaled to produce a facial expression at a set time. From the participant’s expression data, feature variables (i.e., the degree and variance of movement, and vibration level) involving the facial micromovements at the onset of the expression were analyzed. The results indicated significant differences in the feature variables between the real and fake expression conditions. The differences varied according to facial regions as a function of emotions. This study provides appraisal criteria for identifying the authenticity of facial expressions that are applicable to future research and the design of emotion recognition systems.

scholarly journals FaceSync: Open source framework for recording facial expressions with head-mounted cameras

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 702 ◽  
Author(s):  
Jin Hyun Cheong ◽  
Sawyer Brooks ◽  
Luke J. Chang
Keyword(s):  
Facial Expression ◽  
Open Source ◽  
Facial Expressions ◽  
Video Recording ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Video Recordings ◽  
Automated Method ◽  
Open Source Framework ◽  
The Cost

Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings.  Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups, including group interaction experiments.

scholarly journals Facial Expression Recognition Using Computer Vision: A Systematic Review

2019 ◽  
Vol 9 (21) ◽  
pp. 4678 ◽  
Author(s):  
Daniel Canedo ◽  
António J. R. Neves
Keyword(s):  
Systematic Review ◽  
Computer Vision ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Future Research ◽  
Expression Recognition ◽  
Contemporary World

Emotion recognition has attracted major attention in numerous fields because of its relevant applications in the contemporary world: marketing, psychology, surveillance, and entertainment are some examples. It is possible to recognize an emotion through several ways; however, this paper focuses on facial expressions, presenting a systematic review on the matter. In addition, 112 papers published in ACM, IEEE, BASE and Springer between January 2006 and April 2019 regarding this topic were extensively reviewed. Their most used methods and algorithms will be firstly introduced and summarized for a better understanding, such as face detection, smoothing, Principal Component Analysis (PCA), Local Binary Patterns (LBP), Optical Flow (OF), Gabor filters, among others. This review identified a clear difficulty in translating the high facial expression recognition (FER) accuracy in controlled environments to uncontrolled and pose-variant environments. The future efforts in the FER field should be put into multimodal systems that are robust enough to face the adversities of real world scenarios. A thorough analysis on the research done on FER in Computer Vision based on the selected papers is presented. This review aims to not only become a reference for future research on emotion recognition, but also to provide an overview of the work done in this topic for potential readers.

scholarly journals Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

2021 ◽  
Vol 11 (24) ◽  
pp. 11738
Author(s):  
Thomas Teixeira ◽  
Éric Granger ◽  
Alessandro Lameiras Koerich
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Affective Computing ◽  
Spatial Information ◽  
Short Term Memory ◽  
State Of The Art ◽  
Fine Tuning

Facial expressions are one of the most powerful ways to depict specific patterns in human behavior and describe the human emotional state. However, despite the impressive advances of affective computing over the last decade, automatic video-based systems for facial expression recognition still cannot correctly handle variations in facial expression among individuals as well as cross-cultural and demographic aspects. Nevertheless, recognizing facial expressions is a difficult task, even for humans. This paper investigates the suitability of state-of-the-art deep learning architectures based on convolutional neural networks (CNNs) to deal with long video sequences captured in the wild for continuous emotion recognition. For such an aim, several 2D CNN models that were designed to model spatial information are extended to allow spatiotemporal representation learning from videos, considering a complex and multi-dimensional emotion space, where continuous values of valence and arousal must be predicted. We have developed and evaluated convolutional recurrent neural networks, combining 2D CNNs and long short term-memory units and inflated 3D CNN models, which are built by inflating the weights of a pre-trained 2D CNN model during fine-tuning, using application-specific videos. Experimental results on the challenging SEWA-DB dataset have shown that these architectures can effectively be fine-tuned to encode spatiotemporal information from successive raw pixel images and achieve state-of-the-art results on such a dataset.

scholarly journals The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

2021 ◽  
Vol 12 ◽  
Author(s):  
Shu Zhang ◽  
Xinge Liu ◽  
Xuan Yang ◽  
Yezhi Shu ◽  
Niqi Liu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Future Research ◽  
Emotional Expressions ◽  
Facial Features ◽  
Individual Feature ◽  
Human Computer Interactions ◽  
Emotional Information ◽  
Face Images ◽  
Full Face

Cartoon faces are widely used in social media, animation production, and social robots because of their attractive ability to convey different emotional information. Despite their popular applications, the mechanisms of recognizing emotional expressions in cartoon faces are still unclear. Therefore, three experiments were conducted in this study to systematically explore a recognition process for emotional cartoon expressions (happy, sad, and neutral) and to examine the influence of key facial features (mouth, eyes, and eyebrows) on emotion recognition. Across the experiments, three presentation conditions were employed: (1) a full face; (2) individual feature only (with two other features concealed); and (3) one feature concealed with two other features presented. The cartoon face images used in this study were converted from a set of real faces acted by Chinese posers, and the observers were Chinese. The results show that happy cartoon expressions were recognized more accurately than neutral and sad expressions, which was consistent with the happiness recognition advantage revealed in real face studies. Compared with real facial expressions, sad cartoon expressions were perceived as sadder, and happy cartoon expressions were perceived as less happy, regardless of whether full-face or single facial features were viewed. For cartoon faces, the mouth was demonstrated to be a feature that is sufficient and necessary for the recognition of happiness, and the eyebrows were sufficient and necessary for the recognition of sadness. This study helps to clarify the perception mechanism underlying emotion recognition in cartoon faces and sheds some light on directions for future research on intelligent human-computer interactions.

scholarly journals FaceSync: Open source framework for recording facial expressions with head-mounted cameras

2017 ◽  
Author(s):  
Jin Hyun Cheong ◽  
Sawyer Brooks ◽  
Luke J. Chang
Keyword(s):  
Facial Expression ◽  
Open Source ◽  
Facial Expressions ◽  
Video Recording ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Video Recordings ◽  
Automated Method ◽  
Open Source Framework ◽  
The Cost

Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings. Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups including group interaction experiments.

The effect of personality and social context on willingness to share emotion information on social media

2018 ◽  
Author(s):  
Damien Dupré ◽  
Nicole Andelic ◽  
Anna Zajac ◽  
Gawain Morrison ◽  
Gary John McKeown
Keyword(s):  
Social Media ◽  
Social Context ◽  
Facial Expression ◽  
Personality Traits ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Personal Information ◽  
Facial Emotion ◽  
Emotional Information ◽  
New Sensors

Sharing personal information is an important way of communicating on social media. Among the information possibly shared, new sensors and tools allow people to share emotion information via facial emotion recognition. This paper questions whether people are prepared to share personal information such as their own emotion on social media. In the current study we examined how factors such as felt emotion, motivation for sharing on social media as well as personality affected participants’ willingness to share self-reported emotion or facial expression online. By carrying out a GLMM analysis, this study found that participants’ willingness to share self-reported emotion and facial expressions was influenced by their personality traits and the motivation for sharing their emotion information that they were given. From our results we can conclude that the estimated level of privacy for certain emotional information, such as facial expression, is influenced by the motivation for sharing the information online.

scholarly journals Facial Emotion Recognition by Deep CNN and HAAR Cascade

2019 ◽  
Vol 8 (12) ◽  
pp. 3433-3441
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Activation Function ◽  
Facial Emotion ◽  
Facial Features ◽  
Basic Emotions ◽  
Haar Cascade ◽  
Deep Cnn ◽  
The Many

Emotion Recognition is of significance in the modern scenario. Among the many ways to perform it, one of them is through facial expression detection since it is a spontaneous arousal of mental state rather than a conscious effort. Sometimes emotions rule us in the form of the choices, actions and perceptions which are in turn, a result of the emotions we are overpowered by. Happiness, sadness, fear, disgust, anger, neutral and surprise are the seven basic emotions expressed by a human most frequently. In this period of automation and human computer interaction, it is a very difficult and tedious job to make the machines detect the emotions. Facial expressions are the medium through which emotions are shown. For one to detect the facial expression of a person, colour, orientation, lighting and posture play significant importance. Hence, the movements associated with eye, nose, lips etc. plays major role in differentiating the facial features. These facial features are then classified and compared through the trained data. In this paper, we have constructed a Convolution Neural Network (CNN) model and then recognised different emotions for a particular dataset. We have found the accuracy of the model and our main aim is to minimise the loss. We have made use of Adam’s optimizer and used loss function as sparse categorical crossentropy and activation function as softmax. The results which we have got are quite accurate and can be used for further research in this field.

scholarly journals A Review of Emotion Recognition Based on EEG using DEAP Dataset

2021 ◽  
pp. 298-303
Author(s):  
Rama Chaudhary ◽  
Ram Avtar Jaswal
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Physiological Signals ◽  
Emotional States ◽  
Modern Time ◽  
Human Machine Interaction ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Machine Interaction

In modern time, the human-machine interaction technology has been developed so much for recognizing human emotional states depending on physiological signals. The emotional states of human can be recognized by using facial expressions, but sometimes it doesn’t give accurate results. For example, if we detect the accuracy of facial expression of sad person, then it will not give fully satisfied result because sad expression also include frustration, irritation, anger, etc. therefore, it will not be possible to determine the particular expression. Therefore, emotion recognition using Electroencephalogram (EEG), Electrocardiogram (ECG) has gained so much attraction because these are based on brain and heart signals respectively. So, after analyzing all the factors, it is decided to recognize emotional states based on EEG using DEAP Dataset. So that, the better accuracy can be achieved.

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.

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