Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication

In the recent years facial emotion recognition algorithms have evolved and in some cases top commercial algorithms detect emotions like happiness better than humans do. To evaluate the performance of these algorithms, the common practice is to compare them with human-labeled ground truth. This article covers monitoring of the advancements in automatic emotion recognition solutions, and here we suggest an additional criteria for their evaluation, that is the agreement between algorithms’ predictions. In this work, we compare the performance of four commercial algorithms: Affectiva Affdex, Microsoft Cognitive Services Face module Emotion Recognition, Amazon Rekognition Face Analysis, and Neurodata Lab Emotion Recognition on three datasets AFEW, RAVDESS, and SAVEE, that differ in terms of control over conditions of data acquisition. We assume that the consistency among algorithms’ predictions indicates the reliability of the predicted emotion. Overall results show that the algorithms with higher accuracy and f1-scores that were obtained for human-labeled ground truth (Microsoft’s, Neurodata Lab’s, and Amazon’s), showed higher agreement between their predictions. Agreement among algorithms’ predictions is a promising criteria in terms of further exploring the option to replace human data labeling with automatic annotation.

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Emotion Recognition in Affective Tutoring Systems: Collection of Ground-truth Data

Procedia Computer Science ◽

10.1016/j.procs.2017.01.157 ◽

2017 ◽

Vol 104 ◽

pp. 437-444 ◽

Cited By ~ 14

Author(s):

Sintija Petrovica ◽

Alla Anohina-Naumeca ◽

Hazım Kemal Ekenel

Keyword(s):

Emotion Recognition ◽

Ground Truth ◽

Ground Truth Data ◽

Tutoring Systems

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Multi-Modal Learning for Speech Emotion Recognition: An Analysis and Comparison of ASR Outputs with Ground Truth Transcription

10.21437/interspeech.2019-1149 ◽

2019 ◽

Cited By ~ 2

Author(s):

Saurabh Sahu ◽

Vikramjit Mitra ◽

Nadee Seneviratne ◽

Carol Espy-Wilson

Keyword(s):

Emotion Recognition ◽

Ground Truth ◽

Speech Emotion Recognition

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Beeinträchtigtes Erkennen emotionaler Gesichtsausdrücke durch psychiatrische Patientinnen bei Alexithymie

Zeitschrift für Psychiatrie Psychologie und Psychotherapie ◽

10.1024/1661-4747/a000135 ◽

2013 ◽

Vol 61 (1) ◽

pp. 7-15 ◽

Cited By ~ 1

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.

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A Comparison of Simple Structure Rotation Criteria in Temporal Exploratory Factor Analysis for Event-Related Potential Data

Methodology ◽

10.1027/1614-2241/a000175 ◽

2019 ◽

Vol 15 (Supplement 1) ◽

pp. 43-60 ◽

Cited By ~ 3

Author(s):

Florian Scharf ◽

Steffen Nestler

Keyword(s):

Factor Analysis ◽

Exploratory Factor Analysis ◽

Simple Structure ◽

Ground Truth ◽

Event Related Potential ◽

Suitable Alternative ◽

Factor Rotation ◽

Rotation Methods ◽

Simple Structure Rotation ◽

Temporal Overlap

Abstract. It is challenging to apply exploratory factor analysis (EFA) to event-related potential (ERP) data because such data are characterized by substantial temporal overlap (i.e., large cross-loadings) between the factors, and, because researchers are typically interested in the results of subsequent analyses (e.g., experimental condition effects on the level of the factor scores). In this context, relatively small deviations in the estimated factor solution from the unknown ground truth may result in substantially biased estimates of condition effects (rotation bias). Thus, in order to apply EFA to ERP data researchers need rotation methods that are able to both recover perfect simple structure where it exists and to tolerate substantial cross-loadings between the factors where appropriate. We had two aims in the present paper. First, to extend previous research, we wanted to better understand the behavior of the rotation bias for typical ERP data. To this end, we compared the performance of a variety of factor rotation methods under conditions of varying amounts of temporal overlap between the factors. Second, we wanted to investigate whether the recently proposed component loss rotation is better able to decrease the bias than traditional simple structure rotation. The results showed that no single rotation method was generally superior across all conditions. Component loss rotation showed the best all-round performance across the investigated conditions. We conclude that Component loss rotation is a suitable alternative to simple structure rotation. We discuss this result in the light of recently proposed sparse factor analysis approaches.

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