Analysis of Facial Emotion Recognition Technology and Its Effectiveness in Human Interaction

2018 ◽  
pp. 217-226
Author(s):  
Qiuwen Li ◽  
Young Ae Kim
Keyword(s):  
Emotion Recognition ◽  
Human Interaction ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Facial Emotion Recognition and Executive Functions in Fibromyalgia

Pain Medicine ◽  
2021 ◽  
Author(s):  
Cristina Muñoz Ladrón de Guevara ◽  
Gustavo A Reyes del Paso ◽  
María José Fernández-Serrano ◽  
Stefan Duschek
Keyword(s):  
Executive Function ◽  
Executive Functions ◽  
Emotion Recognition ◽  
Recognition Accuracy ◽  
Cognitive Task ◽  
Medication Use ◽  
Human Interaction ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Control Mechanisms

Abstract Objective The ability to accurately identify facial expressions of emotions is crucial in human interaction. While a previous study suggested deficient emotional face recognition in patients with fibromyalgia, not much is known about the origin of this impairment. Against this background, this study investigated the role of executive functions. Executive functions refer to cognitive control mechanisms enabling implementation and coordination of basic mental operations. Deficits in this domain are prevalent in fibromyalgia. Methods Fifty-two fibromyalgia patients and thirty-two healthy individuals completed the Ekman-60 Faces Test, which requires classification of facial displays of happiness, sadness, anger, fear, surprise and disgust. They also completed eight tasks assessing the executive function components of shifting, updating and inhibition. Effects of comorbid depression and anxiety disorders, and medication use, were tested in stratified analyses of patient subgroups. Results Patients made more errors overall than controls in classifying the emotional expressions. Moreover, their recognition accuracy correlated positively with performance on most of the executive function tasks. Emotion recognition did not vary as a function of comorbid psychiatric disorders or medication use. Conclusions The study supports impaired facial emotion recognition in fibromyalgia, which may contribute to the interaction problems and poor social functioning characterizing this condition. Facial emotion recognition is regarded as a complex process, which may be particularly reliant on efficient coordination of various basic operations by executive functions. As such, the correlations between cognitive task performance and recognition accuracy suggest that deficits in higher cognitive functions underlie impaired emotional communication in fibromyalgia.

scholarly journals Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation

2020 ◽  
Author(s):  
Suchitra Saxena ◽  
Shikha Tripathi ◽  
Sudarshan Tsb
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Human Interaction ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Process Automation ◽  
Training Time ◽  
Robotic Process Automation ◽  
Gated Recurrent Units

This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.

Beeinträchtigtes Erkennen emotionaler Gesichtsausdrücke durch psychiatrische Patientinnen bei Alexithymie

2013 ◽  
Vol 61 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Daniel Dittrich ◽  
Gregor Domes ◽  
Susi Loebel ◽  
Christoph Berger ◽  
Carsten Spitzer ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Emotional Expression ◽  
Depression Scale ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Check List ◽  
Psychosomatische Erkrankungen ◽  
Psychiatrische Patienten ◽  
Symptom Check List ◽  
Tas 20

Die vorliegende Studie untersucht die Hypothese eines mit Alexithymie assoziierten Defizits beim Erkennen emotionaler Gesichtsaudrücke an einer klinischen Population. Darüber hinaus werden Hypothesen zur Bedeutung spezifischer Emotionsqualitäten sowie zu Gender-Unterschieden getestet. 68 ambulante und stationäre psychiatrische Patienten (44 Frauen und 24 Männer) wurden mit der Toronto-Alexithymie-Skala (TAS-20), der Montgomery-Åsberg Depression Scale (MADRS), der Symptom-Check-List (SCL-90-R) und der Emotional Expression Multimorph Task (EEMT) untersucht. Als Stimuli des Gesichtererkennungsparadigmas dienten Gesichtsausdrücke von Basisemotionen nach Ekman und Friesen, die zu Sequenzen mit sich graduell steigernder Ausdrucksstärke angeordnet waren. Mittels multipler Regressionsanalyse untersuchten wir die Assoziation von TAS-20 Punktzahl und facial emotion recognition (FER). Während sich für die Gesamtstichprobe und den männlichen Stichprobenteil kein signifikanter Zusammenhang zwischen TAS-20-Punktzahl und FER zeigte, sahen wir im weiblichen Stichprobenteil durch die TAS-20 Punktzahl eine signifikante Prädiktion der Gesamtfehlerzahl (β = .38, t = 2.055, p < 0.05) und den Fehlern im Erkennen der Emotionen Wut und Ekel (Wut: β = .40, t = 2.240, p < 0.05, Ekel: β = .41, t = 2.214, p < 0.05). Für wütende Gesichter betrug die Varianzaufklärung durch die TAS-20-Punktzahl 13.3 %, für angeekelte Gesichter 19.7 %. Kein Zusammenhang bestand zwischen der Zeit, nach der die Probanden die emotionalen Sequenzen stoppten, um ihre Bewertung abzugeben (Antwortlatenz) und Alexithymie. Die Ergebnisse der Arbeit unterstützen das Vorliegen eines mit Alexithymie assoziierten Defizits im Erkennen emotionaler Gesichtsausdrücke bei weiblchen Probanden in einer heterogenen, klinischen Stichprobe. Dieses Defizit könnte die Schwierigkeiten Hochalexithymer im Bereich sozialer Interaktionen zumindest teilweise begründen und so eine Prädisposition für psychische sowie psychosomatische Erkrankungen erklären.

Music to my ears: Age-related decline in musical and facial emotion recognition.

2017 ◽  
Vol 32 (8) ◽  
pp. 698-709 ◽  
Author(s):  
Ryan Sutcliffe ◽  
Peter G. Rendell ◽  
Julie D. Henry ◽  
Phoebe E. Bailey ◽  
Ted Ruffman
Keyword(s):  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Age Related

Task characteristics influence facial emotion recognition age-effects: A meta-analytic review.

2020 ◽  
Vol 35 (2) ◽  
pp. 295-315 ◽  
Author(s):  
Grace S. Hayes ◽  
Skye N. McLennan ◽  
Julie D. Henry ◽  
Louise H. Phillips ◽  
Gill Terrett ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Age Effects ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Task Characteristics ◽  
Analytic Review

Usability of a psychotherapeutic interactive gaming tool used in facial emotion recognition for people with schizophrenia (Preprint)

2019 ◽  
Author(s):  
Ingrid Tortadès ◽  
Roberto Gonzalez ◽  
Francesc Alpiste ◽  
Joaquín Fernandez ◽  
Jordi Torner ◽  
...  
Keyword(s):  
Theory Of Mind ◽  
Emotion Recognition ◽  
Attributional Style ◽  
Pilot Project ◽  
Error Rates ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Interactive Software ◽  
Emotional Recognition ◽  
Attribution Questionnaire

BACKGROUND Emotional Recognition (ER) is one of the areas most affected in people with schizophrenia. However, there are no software tools available for the assessment of ER. The interactive software program ‘Feeling Master’ (a cartoon facial recognition tool) was developed to investigate the deficit in facial emotion recognition (FER) with a sample of patients with schizophrenia in a pilot project framework. OBJECTIVE The aim of the study was to test the usability of ‘Feeling Master’ as a psychotherapeutic interactive gaming tool for the assessment of emotional recognition in people with schizophrenia compared with healthy people, and the relationship between FER, attributional style and theory of mind. METHODS Nineteen individuals with schizophrenia and 17 healthy control (HC) subjects completed the ‘Feeling Master’ including five emotions (happiness, sadness, anger, fear, and surprise). Regarding the group with schizophrenia they were evaluated with the Personal and Situational Attribution Questionnaire (IPSAQ) and the Hinting Task (Theory of Mind) to evaluate social cognition. RESULTS Patients with schizophrenia showed impairments in emotion recognition and they remained slower than the HC in the recognition of each emotion (P<.001). Regarding the impairment in the recognition of each emotion we only found a trend toward significance in error rates on fear discrimination (P=.07). And the correlations between correct response on the ‘Feeling Master’ and the hinting task showed significant values in the correlation of surprise and theory of mind (P=.046). CONCLUSIONS In conclusion, the study puts forward the usability of the ‘Feeling Master’ in FER for people with schizophrenia. These findings lend support to the notion that difficulties in emotion recognition are more prevalent in people with schizophrenia, and that these are associated with impairment in ToM, suggesting the potential utility of the FER in the rehabilitation of people with schizophrenia.

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