An Automated System in ATM Booth Using Face Encoding and Emotion Recognition Process

We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.

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Emotion Recognition by Correlating Facial Expressions and EEG Analysis

Applied Sciences ◽

10.3390/app11156987 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6987

Author(s):

Adrian R. Aguiñaga ◽

Daniel E. Hernandez ◽

Angeles Quezada ◽

Andrés Calvillo Téllez

Keyword(s):

Emotion Recognition ◽

Facial Expressions ◽

Affective Computing ◽

A Priori ◽

Computing System ◽

Emotional States ◽

Recognition Process ◽

Non Invasive ◽

Brain Signals ◽

Proposed Model

Emotion recognition is a fundamental task that any affective computing system must perform to adapt to the user’s current mood. The analysis of electroencephalography signals has gained notoriety in studying human emotions because of its non-invasive nature. This paper presents a two-stage deep learning model to recognize emotional states by correlating facial expressions and brain signals. Most of the works related to the analysis of emotional states are based on analyzing large segments of signals, generally as long as the evoked potential lasts, which could cause many other phenomena to be involved in the recognition process. Unlike with other phenomena, such as epilepsy, there is no clearly defined marker of when an event begins or ends. The novelty of the proposed model resides in the use of facial expressions as markers to improve the recognition process. This work uses a facial emotion recognition technique (FER) to create identifiers each time an emotional response is detected and uses them to extract segments of electroencephalography (EEG) records that a priori will be considered relevant for the analysis. The proposed model was tested on the DEAP dataset.

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Morphing Task: The Emotion Recognition Process in Children with Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182413273 ◽

2021 ◽

Vol 18 (24) ◽

pp. 13273

Author(s):

Cristina Greco ◽

Maria Romani ◽

Anna Berardi ◽

Gloria De Vita ◽

Giovanni Galeoto ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Attention Deficit Hyperactivity Disorder ◽

Emotion Recognition ◽

Attention Deficit ◽

Control Sample ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Chi Square ◽

Recognition Process ◽

Hyperactivity Disorder

Recognizing a person’s identity is a fundamental social ability; facial expressions, in particular, are extremely important in social cognition. Individuals affected by autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) display impairment in the recognition of emotions and, consequently, in recognizing expressions related to emotions, and even their identity. The aim of our study was to compare the performance of participants with ADHD, ASD, and typical development (TD) with regard to both accuracy and speed in the morphing task and to determine whether the use of pictures of digitized cartoon faces could significantly facilitate the process of emotion recognition in ASD patients (particularly for disgust). This study investigated the emotion recognition process through the use of dynamic pictures (human faces vs. cartoon faces) created with the morphing technique in three pediatric populations (7–12 years old): ADHD patients, ASD patients, and an age-matched control sample (TD). The Chi-square test was used to compare response latency and accuracy between the three groups in order to determine if there were statistically significant differences (p < 0.05) in the recognition of basic emotions. The results demonstrated a faster response time in neurotypical children compared to ASD and ADHD children, with ADHD participants performing better than ASD participants on the same task. The overall accuracy parameter between the ADHD and ASD groups did not significantly differ.

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Fluency Board Streamlines Recognition Process

ASHA Leader ◽

10.1044/leader.an3.15062010.22 ◽

2010 ◽

Vol 15 (6) ◽

pp. 22-23

Author(s):

James McClure ◽

Chamonix Olsen

Keyword(s):

Recognition Process

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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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