scholarly journals FACIAL EXPRESSIONS RECOGNITION IN PHOTOGRAPHS, DRAWINGS, AND EMOTICONS

1970 ◽  
Vol 13 (3) ◽  
pp. 293-309
Author(s):  
Senka Kostić ◽  
Tijana Todić Jakšić ◽  
Oliver Tošković
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Visual Presentation ◽  
Human Face ◽  
Expression Of Emotions ◽  
Facial Expressions Recognition

Results of previous studies point to the importance of different face parts for certain emotion recognition, and also show that emotions are better recognized in photographs than in caricatures of faces. Therefore, the aim of the study was to examine the accuracy of recognizing facial expression of emotions in relation to the type of emotion and the type of visual presentations. Stimuli contained facial expressions, shown as a photograph, face drawing, or as an emoticon. The task for the participant was to click on the emotion he thought was shown on the stimulus. As factors, the type of displayed emotion varied (happiness, sorrow, surprise, anger, disgust, fear), as well as the type of visual presentation (photo of a human face, a drawing of a human face and an emoticon). As the dependent variable, we used the number of accurately recognized facial expressions in all 18 situations. The results showed that there is an interaction of the type of emotion being evaluated and the type of visual presentation, F(10; 290) = 10.55, p < .01, ŋ2 = .27. The facial expression of fear was most accurately assessed in the drawing of the human face. Emotion of sorrow was most accurately recognized in the assessment of emoticon, and the expression of disgust was recognized worst on the emoticon. Other expressions of emotions were equally well assessed independently of the type of visual presentation. The type of visual presentation has proven to be important for recognizing some emoticons, but not for all of them. 

scholarly journals Transmitting and Decoding Facial Expressions

2005 ◽  
Vol 16 (3) ◽  
pp. 184-189 ◽  
Author(s):  
Marie L. Smith ◽  
Garrison W. Cottrell ◽  
FrédéAric Gosselin ◽  
Philippe G. Schyns
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Brain Structures ◽  
Human Face ◽  
The Face ◽  
The Brain

This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

Somatic Semiotics: Emotion and the Human Face in the Sagas andÞættirof Icelanders

Traditio ◽  
2014 ◽  
Vol 69 ◽  
pp. 125-145
Author(s):  
Kirsten Wolf
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Internal State ◽  
Old Norse ◽  
Human Face ◽  
Affective States ◽  
Mixed Emotions ◽  
Wide Range ◽  
Point Of Departure ◽  
Human Facial Expression

The human face has the capacity to generate expressions associated with a wide range of affective states. Despite the fact that there are few words to describe human facial behaviors, the facial muscles allow for more than a thousand different facial appearances. Some examples of feelings that can be expressed are anger, concentration, contempt, excitement, nervousness, and surprise. Regardless of culture or language, the same expressions are associated with the same emotions and vary only in intensity. Using modern psychological analyses as a point of departure, this essay examines descriptions of human facial expressions as well as such bodily “symptoms” as flushing, turning pale, and weeping in Old Norse-Icelandic literature. The aim is to analyze the manner in which facial signs are used as a means of non-verbal communication to convey the impression of an individual's internal state to observers. More specifically, this essay seeks to determine when and why characters in these works are described as expressing particular facial emotions and, especially, the range of emotions expressed. The Sagas andþættirof Icelanders are in the forefront of the analysis and yield well over one hundred references to human facial expression and color. The examples show that through gaze, smiling, weeping, brows that are raised or knitted, and coloration, the Sagas andþættirof Icelanders tell of happiness or amusement, pleasant and unpleasant surprise, fear, anger, rage, sadness, interest, concern, and even mixed emotions for which language has no words. The Sagas andþættirof Icelanders may be reticent in talking about emotions and poor in emotional vocabulary, but this poverty is compensated for by making facial expressions signifiers of emotion. This essay makes clear that the works are less emotionally barren than often supposed. It also shows that our understanding of Old Norse-Icelandic “somatic semiotics” may well depend on the universality of facial expressions and that culture-specific “display rules” or “elicitors” are virtually nonexistent.

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 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.

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.

The facial expression of emotions recognition in patients with polycystic ovary syndrome

2016 ◽  
Vol 62 (5) ◽  
pp. 35
Author(s):  
Adam Czyzyk ◽  
Kinga Polak ◽  
Agnieszka Podfigurna ◽  
Stanislaw Kozlowski ◽  
Blazej Meczekalski
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Facial Expression ◽  
Facial Expressions ◽  
Polycystic Ovary ◽  
Control Group ◽  
Eysenck Personality Questionnaire ◽  
Ovary Syndrome ◽  
Pcos Group ◽  
Expression Of Emotions ◽  
Time Required

Background. A facial expression of emotions recognition is one of the basic psychological abilities. Sex steroids are able to strongly modulate the process of interpretation of facial expressions, as it has been shown in Turner syndrome patients.Objective. The aim of this study was the assessment of ability to interpret the facial emotions in women with polycystic ovary syndrome (PCOS).Methods. Participants completed a visual emotional task in which they were asked to recognize the emotion expressed of 80 randomly chosen facial expressions from NimStim set (Tottenham et al., 2009). With dedicated software we were able to assess the accuracy of patients facial emotion recognition (in comparison to NimStim validation set) and time required to provide the answer. Patients with psychotic personality have been excluded using Eysenck Personality Questionnaire (EPQ). All the patients underwent also hormonal tests including gonadotropins, estradiol and androgen concentrations.Patients. 80 women diagnosed with PCOS and hyperandrogenemia were included to the study. The control group consisted of 60 healthy, euovulatory women matched by age.Intervention. Each patient underwent visual emotional and EPQ tasks using specifically designed software.Main outcome measures. The accuracy rate (AR) and time required to recognize emotion (TE) of following emotions: anger, disgust, fear, happiness, sadness, surprise, calm and neutral has been measured.Results. Patients with PCOS showed significantly reduced AR for calm (0.76¬+/-0.09) and surprise (0.67+/-0.18) emotions in comparison to controls (0.81+/-0.09, 0.79+/-0.08 respectively). The TE for the anger was higher in PCOS group. Estradiol concentrations showed a statistic tendency (p=0.07) for correlation with TE for the happiness in controls. Conclusions. In this study we showed for the first time that patients affected by hyperandrogenism shows signs of disturbed recognition of facial expression of emotions. 

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.

Priming of Emotion Recognition

2005 ◽  
Vol 58 (7) ◽  
pp. 1173-1197 ◽  
Author(s):  
Naomi C. Carroll ◽  
Andrew W. Young
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Emotional Word ◽  
Classification Task ◽  
Emotion Classification ◽  
Emotional Stimuli ◽  
No Inhibition ◽  
Emotional Category ◽  
Prime Target

Four experiments investigated priming of emotion recognition using a range of emotional stimuli, including facial expressions, words, pictures, and nonverbal sounds. In each experiment, a prime–target paradigm was used with related, neutral, and unrelated pairs. In Experiment 1, facial expression primes preceded word targets in an emotion classification task. A pattern of priming of emotional word targets by related primes with no inhibition of unrelated primes was found. Experiment 2 reversed these primes and targets and found the same pattern of results, demonstrating bidirectional priming between facial expressions and words. Experiment 2 also found priming of facial expression targets by picture primes. Experiment 3 demonstrated that priming occurs not just between pairs of stimuli that have a high co-occurrence in the environment (for example, nonverbal sounds and facial expressions), but with stimuli that co-occur less frequently and are linked mainly by their emotional category (for example, nonverbal sounds and printed words). This shows the importance of the prime and target sharing a common emotional category, rather than their previous co-occurrence. Experiment 4 extended the findings by showing that there are category-based effects as well as valence effects in emotional priming, supporting a categorical view of emotion recognition.

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