Patterns of Emotion Attribution are Affected in Patients with Schizophrenia

2015 ◽  
Vol 18 ◽  
Author(s):  
María Verónica Romero-Ferreiro ◽  
Luis Aguado ◽  
Javier Rodriguez-Torresano ◽  
Tomás Palomo ◽  
Roberto Rodriguez-Jimenez
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Expressions ◽  
Facial Affect Recognition ◽  
Emotional States ◽  
Cognitive Representation ◽  
Novel Approach ◽  
Emotion Label ◽  
Poorly Differentiated ◽  
Emotion Labels

AbstractDeficits in facial affect recognition have been repeatedly reported in schizophrenia patients. The hypothesis that this deficit is caused by poorly differentiated cognitive representation of facial expressions was tested in this study. To this end, performance of patients with schizophrenia and controls was compared in a new emotion-rating task. This novel approach allowed the participants to rate each facial expression at different times in terms of different emotion labels. Results revealed that patients tended to give higher ratings to emotion labels that did not correspond to the portrayed emotion, especially in the case of negative facial expressions (p < .001, η2 = .131). Although patients and controls gave similar ratings when the emotion label matched with the facial expression, patients gave higher ratings on trials with "incorrect" emotion labels (ps < .05). Comparison of patients and controls in a summary index of expressive ambiguity showed that patients perceived angry, fearful and happy faces as more emotionally ambiguous than did the controls (p < .001, η2 = .135). These results are consistent with the idea that the cognitive representation of emotional expressions in schizophrenia is characterized by less clear boundaries and a less close correspondence between facial configurations and emotional states.

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.

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

2013 ◽  
pp. 42-55
Author(s):  
Yi Ji ◽  
Khalid Idrissi
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Video Sequences ◽  
Polynomial Kernel ◽  
Expression Recognition ◽  
Emotional States ◽  
Small Set

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

scholarly journals The influence of dynamics and speech on understanding humanoid facial expressions

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878315 ◽  
Author(s):  
Nicole Lazzeri ◽  
Daniele Mazzei ◽  
Maher Ben Moussa ◽  
Nadia Magnenat-Thalmann ◽  
Danilo De Rossi
Keyword(s):  
Facial Expression ◽  
Social Interactions ◽  
Facial Expressions ◽  
Humanoid Robots ◽  
Emotional States ◽  
Human Communication ◽  
Dynamic Stimuli ◽  
3 Dimensional ◽  
Recognition Of Facial Expressions ◽  
Virtual Avatar

Human communication relies mostly on nonverbal signals expressed through body language. Facial expressions, in particular, convey emotional information that allows people involved in social interactions to mutually judge the emotional states and to adjust its behavior appropriately. First studies aimed at investigating the recognition of facial expressions were based on static stimuli. However, facial expressions are rarely static, especially in everyday social interactions. Therefore, it has been hypothesized that the dynamics inherent in a facial expression could be fundamental in understanding its meaning. In addition, it has been demonstrated that nonlinguistic and linguistic information can contribute to reinforce the meaning of a facial expression making it easier to be recognized. Nevertheless, few studies have been performed on realistic humanoid robots. This experimental work aimed at demonstrating the human-like expressive capability of a humanoid robot by examining whether the effect of motion and vocal content influenced the perception of its facial expressions. The first part of the experiment aimed at studying the recognition capability of two kinds of stimuli related to the six basic expressions (i.e. anger, disgust, fear, happiness, sadness, and surprise): static stimuli, that is, photographs, and dynamic stimuli, that is, video recordings. The second and third parts were focused on comparing the same six basic expressions performed by a virtual avatar and by a physical robot under three different conditions: (1) muted facial expressions, (2) facial expressions with nonlinguistic vocalizations, and (3) facial expressions with an emotionally neutral verbal sentence. The results show that static stimuli performed by a human being and by the robot were more ambiguous than the corresponding dynamic stimuli on which motion and vocalization were associated. This hypothesis has been also investigated with a 3-dimensional replica of the physical robot demonstrating that even in case of a virtual avatar, dynamic and vocalization improve the emotional conveying capability.

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.

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

2011 ◽  
Vol 1 (1) ◽  
pp. 41-54 ◽  
Author(s):  
Yi Ji ◽  
Khalid Idrissi
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Polynomial Kernel ◽  
Expression Recognition ◽  
Emotional States ◽  
New Methods ◽  
Small Set ◽  
Better Than

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

scholarly journals Facial Expression Recognition System Using Facial Characteristic Points And ID3

2014 ◽  
pp. 109-113
Author(s):  
Shubhrata Gupta ◽  
Keshri Verma ◽  
Nazil Perveen
Keyword(s):  
Decision Tree ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Beings ◽  
Novel Approach ◽  
Facial Characteristic ◽  
Characteristic Points

Facial expression is one of the most powerful, natural, and abrupt means for human beings which have the knack to communicate emotion and regulate inter-personal behaviour. In this paper we present a novel approach for facial expression detection using decision tree. Facial expression information is mostly concentrate on facial expression information regions, so the mouth, eye and eyebrow regions are segmented from the facial expression images firstly. Using these templates we calculate 30 facial characteristics points (FCP’s). These facial characteristic points describe the position and shape of the above three organs to find diverse parameters which are input to the decision tree for recognizing different facial expressions.

scholarly journals Using emotional scripts to generate and validate a set of emotion verbs

2015 ◽  
Author(s):  
Emma Portch ◽  
Jelena Havelka ◽  
Charity Brown ◽  
Roger Giner-Sorolla
Keyword(s):  
Emotional States ◽  
Emotional Words ◽  
Discrete Emotion ◽  
Emotion Label ◽  
The Stability ◽  
Verb Meaning ◽  
Degree Of Association ◽  
Internal Context ◽  
The Relationship ◽  
Emotion Labels

Information about everyday emotional experiences is integrated into internal scripts (e.g. Shaver et al., 1987). Script content provides a context within which to compare and subsequently interpret newly experienced, emotional stimuli, such as facial expressions and behaviours. We explore whether this internal context may also be used to interpret emotional words. In particular, we argue that the ‘meaning’ of emotional verbs may be strongly context-dependent (e.g. Schacht & Sommer, 2009). Harnessing previous context-based methods, we define verb meaning by the degree of association between the behaviours to which they refer and discrete emotional states (e.g. ‘fear’), within emotional scripts (Stevenson, Mikels & James, 2007). We used a self-generation method to derive a set of verbs that participants associated with six universal, emotional states (study 1; see full list in appendix A). Emotion labels acted as script anchors. For each verb, degree of emotionality and discrete association were measured by the number of participants who generated that word. As expected, a different modal exemplar was generated for each discrete emotion. In study 2 we used a rating task to assess the stability of the relationship between modal, or typical, verbs and the emotion label to which they had been generated. Verbs and labels were embedded in a sentence and participants were invited to reflect on their emotional attributions in everyday life to rate the association (‘If you are feeling ‘sad’ how likely would you be to act in the following way?’ e.g. ’cry’). Findings suggest that typical relationships were robust. Participants always gave higher ratings to typical vs. atypical verb and label pairings even when (a) rating direction was manipulated (the label or verb appeared first in the sentence), and (b) the typical behaviours were to be performed by themselves or others ( ‘If someone is sad, how likely are they to act in the following way?’ e.g. ’cry’). Our findings suggest that emotion scripts create verb meaning, and therefore provide a context within which to interpret emotional words. We provide a set of emotion verbs that are robustly associated with discrete, emotional labels/states. This resource may be used by a variety of researchers, including those interested in categorical processing of emotional words and language-mediated facial mimicry.

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.

Suppression of Facial Mimicry of Negative Facial Expressions in an Incongruent Context

2018 ◽  
Vol 32 (4) ◽  
pp. 160-171 ◽  
Author(s):  
Léonor Philip ◽  
Jean-Claude Martin ◽  
Céline Clavel
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Affective Priming ◽  
Emotional Expressions ◽  
Facial Electromyography ◽  
Emotional Cues ◽  
Priming Task ◽  
Priming Word ◽  
Affective Priming Task ◽  
Neutral Expression

Abstract. People react with Rapid Facial Reactions (RFRs) when presented with human facial emotional expressions. Recent studies show that RFRs are not always congruent with emotional cues. The processes underlying RFRs are still being debated. In our study described herein, we manipulate the context of perception and its influence on RFRs. We use a subliminal affective priming task with emotional labels. Facial electromyography (EMG) (frontalis, corrugator, zygomaticus, and depressor) was recorded while participants observed static facial expressions (joy, fear, anger, sadness, and neutral expression) preceded/not preceded by a subliminal word (JOY, FEAR, ANGER, SADNESS, or NEUTRAL). For the negative facial expressions, when the priming word was congruent with the facial expression, participants displayed congruent RFRs (mimicry). When the priming word was incongruent, we observed a suppression of mimicry. Happiness was not affected by the priming word. RFRs thus appear to be modulated by the context and type of emotion that is presented via facial expressions.

Neural processing of facial expressions as modulators of communicative intention

2021 ◽  
Author(s):  
Jalil Rasgado-Toledo ◽  
Elizabeth Valles-Capetillo ◽  
Averi Giudicessi ◽  
Magda Giordano
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Speech Acts ◽  
Traditional Approach ◽  
Contextual Information ◽  
Brain Regions ◽  
Emotional Expressions ◽  
Brain Response ◽  
Communicative Intention ◽  
Neuroimaging Data

Speakers use a variety of contextual information, such as facial emotional expressions for the successful transmission of their message. Listeners must decipher the meaning by understanding the intention behind it (Recanati, 1986). A traditional approach to the study of communicative intention has been through speech acts (Escandell, 2006). The objective of the present study is to further the understanding of the influence of facial expression to the recognition of communicative intention. The study sought to: verify the reliability of facial expressions recognition, find if there is an association between a facial expression and a category of speech acts, test if words contain an intentional load independent of the facial expression presented, and test whether facial expressions can modify an utterance’s communicative intention and the neural correlates associated using univariate and multivariate approaches. We found that previous observation of facial expressions associated with emotions can modify the interpretation of an assertive utterance that followed the facial expression. The hemodynamic brain response to an assertive utterance was moderated by the preceding facial expression and that classification based on the emotions expressed by the facial expression could be decoded by fluctuations in the brain’s hemodynamic response during the presentation of the assertive utterance. Neuroimaging data showed activation of regions involved in language, intentionality and face recognition during the utterance’s reading. Our results indicate that facial expression is a relevant contextual cue that decodes the intention of an utterance, and during decoding it engages different brain regions in agreement with the emotion expressed.

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