scholarly journals Acting Surprised: Comparing Perceptions of Different Dynamic Deliberate Expressions

2020 ◽  
Author(s):  
Mircea Zloteanu ◽  
Eva G. Krumhuber ◽  
Daniel C. Richardson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Affective State ◽  
External Condition ◽  
Internal Condition ◽  
Minimal Effort ◽  
Better Than

AbstractPeople are accurate at classifying emotions from facial expressions but much poorer at determining if such expressions are spontaneously felt or deliberately posed. We explored if the method used by senders to produce an expression influences the decoder’s ability to discriminate authenticity, drawing inspiration from two well-known acting techniques: the Stanislavski (internal) and Mimic method (external). We compared spontaneous surprise expressions in response to a jack-in-the-box (genuine condition), to posed displays of senders who either focused on their past affective state (internal condition) or the outward expression (external condition). Although decoders performed better than chance at discriminating the authenticity of all expressions, their accuracy was lower in classifying external surprise compared to internal surprise. Decoders also found it harder to discriminate external surprise from spontaneous surprise and were less confident in their decisions, perceiving these to be similarly intense but less genuine-looking. The findings suggest that senders are capable of voluntarily producing genuine-looking expressions of emotions with minimal effort, especially by mimicking a genuine expression. Implications for research on emotion recognition are discussed.

scholarly journals Acting surprised: Comparing perceptions of different dynamic deliberate expressions

2019 ◽  
Author(s):  
Mircea Zloteanu ◽  
Eva Krumhuber ◽  
Daniel C. Richardson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Affective State ◽  
External Condition ◽  
Internal Condition ◽  
Minimal Effort ◽  
Better Than

People are accurate at classifying emotions from facial expressions but much poorer at determining if such expressions are spontaneously felt or deliberately posed. We explored if the method used by senders to produce an expression influences the decoder’s ability to discriminate authenticity, drawing inspiration from two well-known acting techniques: the Stanislavski (internal) and Mimic method (external). We compared spontaneous surprise expressions in response to a jack-in-the-box (genuine condition), to posed displays of senders who either focused on their past affective state (internal condition) or the outward expression (external condition). Although decoders performed better than chance at discriminating the authenticity of all expressions, their accuracy was lower in classifying external surprise compared to internal surprise. Decoders also found it harder to discriminate external surprise from spontaneous surprise and were less confident in their decisions, perceiving these to be similarly intense but less genuine-looking. The findings suggest that senders are capable of voluntarily producing genuine-looking expressions of emotions with minimal effort, especially by mimicking a genuine expression. Implications for research on emotion recognition are discussed.

scholarly journals GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks

2021 ◽  
Vol 38 (6) ◽  
pp. 1689-1698
Author(s):  
Suat Toraman ◽  
Ömer Osman Dursun
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Classification Accuracy ◽  
Single Channel ◽  
Visual Stimuli ◽  
Classification Performance ◽  
Eeg Signals ◽  
Machine Learning Methods ◽  
First Time ◽  
Better Than

Human emotion recognition with machine learning methods through electroencephalographic (EEG) signals has become a highly interesting subject for researchers. Although it is simple to define emotions that can be expressed physically such as speech, facial expressions, and gestures, it is more difficult to define psychological emotions that are expressed internally. The most important stimuli in revealing inner emotions are aural and visual stimuli. In this study, EEG signals using both aural and visual stimuli were examined and emotions were evaluated in both binary and multi-class emotion recognitions models. A general emotion recognition model was proposed for non-subject-based classification. Unlike in previous studies, a subject-based testing was performed for the first time in the literature. Capsule Networks, a new neural network model, has been developed for binary and multi-class emotion recognition. In the proposed method, a novel fusion strategy was introduced for binary-class emotion recognition and the model was tested using the GAMEEMO dataset. Binary-class emotion recognition achieved a classification accuracy which was 10% better than the classification performance achieved in other studies in the literature. Based on these findings, we suggest that the proposed method will bring a different perspective to emotion recognition.

scholarly journals Two-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition

2020 ◽  
Vol 24 (6) ◽  
pp. 792-801
Author(s):  
Chang Liu ◽  
◽  
Kaoru Hirota ◽  
Bo Wang ◽  
Yaping Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Affective State ◽  
Texture Feature ◽  
Service Robots ◽  
Facial Features ◽  
Expression Recognition

An emotion recognition framework based on a two-channel convolutional neural network (CNN) is proposed to detect the affective state of humans through facial expressions. The framework consists of three parts, i.e., the frontal face detection module, the feature extraction module, and the classification module. The feature extraction module contains two channels: one is for raw face images and the other is for texture feature images. The local binary pattern (LBP) images are utilized for texture feature extraction to enrich facial features and improve the network performance. The attention mechanism is adopted in both CNN feature extraction channels to highlight the features that are related to facial expressions. Moreover, arcface loss function is integrated into the proposed network to increase the inter-class distance and decrease the inner-class distance of facial features. The experiments conducted on the two public databases, FER2013 and CK+, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 72.56% and 94.24%, respectively. The improvement in emotion recognition accuracy makes our approach applicable to service robots.

Emotion Recognition of Facial Expressions Presented in Profile

2021 ◽  
pp. 003329412110184
Author(s):  
Paola Surcinelli ◽  
Federica Andrei ◽  
Ornella Montebarocci ◽  
Silvana Grandi
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Response Times ◽  
Recognition Accuracy ◽  
Recognition Of Facial Expressions ◽  
Profile View ◽  
The Face ◽  
Within Subjects ◽  
Face Stimuli ◽  
Affective Information

Aim of the research The literature on emotion recognition from facial expressions shows significant differences in recognition ability depending on the proposed stimulus. Indeed, affective information is not distributed uniformly in the face and recent studies showed the importance of the mouth and the eye regions for a correct recognition. However, previous studies used mainly facial expressions presented frontally and studies which used facial expressions in profile view used a between-subjects design or children faces as stimuli. The present research aims to investigate differences in emotion recognition between faces presented in frontal and in profile views by using a within subjects experimental design. Method The sample comprised 132 Italian university students (88 female, Mage = 24.27 years, SD = 5.89). Face stimuli displayed both frontally and in profile were selected from the KDEF set. Two emotion-specific recognition accuracy scores, viz., frontal and in profile, were computed from the average of correct responses for each emotional expression. In addition, viewing times and response times (RT) were registered. Results Frontally presented facial expressions of fear, anger, and sadness were significantly better recognized than facial expressions of the same emotions in profile while no differences were found in the recognition of the other emotions. Longer viewing times were also found when faces expressing fear and anger were presented in profile. In the present study, an impairment in recognition accuracy was observed only for those emotions which rely mostly on the eye regions.

scholarly journals Towards estimating affective states in Virtual Reality based on behavioral data

2021 ◽  
Author(s):  
Valentin Holzwarth ◽  
Johannes Schneider ◽  
Joshua Handali ◽  
Joy Gisler ◽  
Christian Hirt ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Adaptive Systems ◽  
User Study ◽  
Affective State ◽  
Head Movements ◽  
Affective States ◽  
Physical Demand ◽  
Perceived Performance ◽  
Sequential Administration ◽  
Better Than

AbstractInferring users’ perceptions of Virtual Environments (VEs) is essential for Virtual Reality (VR) research. Traditionally, this is achieved through assessing users’ affective states before and after being exposed to a VE, based on standardized, self-assessment questionnaires. The main disadvantage of questionnaires is their sequential administration, i.e., a user’s affective state is measured asynchronously to its generation within the VE. A synchronous measurement of users’ affective states would be highly favorable, e.g., in the context of adaptive systems. Drawing from nonverbal behavior research, we argue that behavioral measures could be a powerful approach to assess users’ affective states in VR. In this paper, we contribute by providing methods and measures evaluated in a user study involving 42 participants to assess a users’ affective states by measuring head movements during VR exposure. We show that head yaw significantly correlates with presence, mental and physical demand, perceived performance, and system usability. We also exploit the identified relationships for two practical tasks that are based on head yaw: (1) predicting a user’s affective state, and (2) detecting manipulated questionnaire answers, i.e., answers that are possibly non-truthful. We found that affective states can be predicted significantly better than a naive estimate for mental demand, physical demand, perceived performance, and usability. Further, manipulated or non-truthful answers can also be estimated significantly better than by a naive approach. These findings mark an initial step in the development of novel methods to assess user perception of VEs.

scholarly journals Negative parental emotional environment increases the association between childhood behavioral problems and impaired recognition of negative facial expressions

2021 ◽  
pp. 1-10
Author(s):  
Daniel T. Burley ◽  
Christopher W. Hobson ◽  
Dolapo Adegboye ◽  
Katherine H. Shelton ◽  
Stephanie H.M. van Goozen
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Behavioral Problems ◽  
Expressed Emotion ◽  
Interactive Effect ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Environment ◽  
Negative Comments ◽  
Child Behavioral Problems

Abstract Impaired facial emotion recognition is a transdiagnostic risk factor for a range of psychiatric disorders. Childhood behavioral difficulties and parental emotional environment have been independently associated with impaired emotion recognition; however, no study has examined the contribution of these factors in conjunction. We measured recognition of negative (sad, fear, anger), neutral, and happy facial expressions in 135 children aged 5–7 years referred by their teachers for behavioral problems. Parental emotional environment was assessed for parental expressed emotion (EE) – characterized by negative comments, reduced positive comments, low warmth, and negativity towards their child – using the 5-minute speech sample. Child behavioral problems were measured using the teacher-informant Strengths and Difficulties Questionnaire (SDQ). Child behavioral problems and parental EE were independently associated with impaired recognition of negative facial expressions specifically. An interactive effect revealed that the combination of both factors was associated with the greatest risk for impaired recognition of negative faces, and in particular sad facial expressions. No relationships emerged for the identification of happy facial expressions. This study furthers our understanding of multidimensional processes associated with the development of facial emotion recognition and supports the importance of early interventions that target this domain.

scholarly journals Effects of early institutionalization on emotion processing in 12-year-old youth

2017 ◽  
Vol 29 (5) ◽  
pp. 1749-1761 ◽  
Author(s):  
Johanna Bick ◽  
Rhiannon Luyster ◽  
Nathan A. Fox ◽  
Charles H. Zeanah ◽  
Charles A. Nelson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Positive Impact ◽  
Developmental Trajectories ◽  
Emotion Processing ◽  
Institutional Care ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Facial Expressions ◽  
Fearful Faces

AbstractWe examined facial emotion recognition in 12-year-olds in a longitudinally followed sample of children with and without exposure to early life psychosocial deprivation (institutional care). Half of the institutionally reared children were randomized into foster care homes during the first years of life. Facial emotion recognition was examined in a behavioral task using morphed images. This same task had been administered when children were 8 years old. Neutral facial expressions were morphed with happy, sad, angry, and fearful emotional facial expressions, and children were asked to identify the emotion of each face, which varied in intensity. Consistent with our previous report, we show that some areas of emotion processing, involving the recognition of happy and fearful faces, are affected by early deprivation, whereas other areas, involving the recognition of sad and angry faces, appear to be unaffected. We also show that early intervention can have a lasting positive impact, normalizing developmental trajectories of processing negative emotions (fear) into the late childhood/preadolescent period.

scholarly journals Recognition of Emotion from Facial Expressions with Direct or Averted Eye Gaze and Varying Expression Intensities in Children with Autism Disorder and Typically Developing Children

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Dina Tell ◽  
Denise Davidson ◽  
Linda A. Camras
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Eye Gaze ◽  
Children With Autism ◽  
Gaze Direction ◽  
Emotional Expressions ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Expression Intensity ◽  
Autism Disorder

Eye gaze direction and expression intensity effects on emotion recognition in children with autism disorder and typically developing children were investigated. Children with autism disorder and typically developing children identified happy and angry expressions equally well. Children with autism disorder, however, were less accurate in identifying fear expressions across intensities and eye gaze directions. Children with autism disorder rated expressions with direct eyes, and 50% expressions, as more intense than typically developing children. A trend was also found for sad expressions, as children with autism disorder were less accurate in recognizing sadness at 100% intensity with direct eyes than typically developing children. Although the present research showed that children with autism disorder are sensitive to eye gaze direction, impairments in the recognition of fear, and possibly sadness, exist. Furthermore, children with autism disorder and typically developing children perceive the intensity of emotional expressions differently.

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