scholarly journals An Exploratory Study on Cross-Cultural Differences in Facial Emotion Recognition Between Adults From Malaysia and Australia

2021 ◽  
Vol 12 ◽  
Author(s):  
Sindhu Nair Mohan ◽  
Firdaus Mukhtar ◽  
Laura Jobson
Keyword(s):  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Major Depressive ◽  
Future Studies ◽  
Cross Cultural Differences ◽  
Depression Status ◽  
European Heritage ◽  
The Difference ◽  
Facial Images

While culture and depression influence the way in which humans process emotion, these two areas of investigation are rarely combined. Therefore, the aim of this study was to investigate the difference in facial emotion recognition among Malaysian Malays and Australians with a European heritage with and without depression. A total of 88 participants took part in this study (Malays n = 47, Australians n = 41). All participants were screened using The Structured Clinical Interview for DSM-5 Clinician Version (SCID-5-CV) to assess the Major Depressive Disorder (MDD) diagnosis and they also completed the Beck Depression Inventory (BDI). This study consisted of the facial emotion recognition (FER) task whereby the participants were asked to look at facial images and determine the emotion depicted by each of the facial expressions. It was found that depression status and cultural group did not significantly influence overall FER accuracy. Malaysian participants without MDD and Australian participants with MDD performed quicker as compared to Australian participants without MDD on the FER task. Also, Malaysian participants more accurately recognized fear as compared to Australian participants. Future studies can focus on the extent of the influence and other aspects of culture and participant condition on facial emotion recognition.

scholarly journals Two-Stage Recognition and beyond for Compound Facial Emotion Recognition

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2847
Author(s):  
Dorota Kamińska ◽  
Kadir Aktas ◽  
Davit Rizhinashvili ◽  
Danila Kuklyanov ◽  
Abdallah Hussein Sham ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Complex Problem ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Facial Features ◽  
Recognition Method ◽  
Two Stage ◽  
Facial Images ◽  
Emotion Labels

Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

scholarly journals The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2026
Author(s):  
Jung Hwan Kim ◽  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
System Performance ◽  
Autonomous Vehicles ◽  
Confusion Matrix ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Facial Image ◽  
Facial Images

Facial emotion recognition (FER) systems play a significant role in identifying driver emotions. Accurate facial emotion recognition of drivers in autonomous vehicles reduces road rage. However, training even the advanced FER model without proper datasets causes poor performance in real-time testing. FER system performance is heavily affected by the quality of datasets than the quality of the algorithms. To improve FER system performance for autonomous vehicles, we propose a facial image threshing (FIT) machine that uses advanced features of pre-trained facial recognition and training from the Xception algorithm. The FIT machine involved removing irrelevant facial images, collecting facial images, correcting misplacing face data, and merging original datasets on a massive scale, in addition to the data-augmentation technique. The final FER results of the proposed method improved the validation accuracy by 16.95% over the conventional approach with the FER 2013 dataset. The confusion matrix evaluation based on the unseen private dataset shows a 5% improvement over the original approach with the FER 2013 dataset to confirm the real-time testing.

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
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