scholarly journals Related Influencing Factors of Deaf People’s Facial Emotion Recognition

2019 ◽  
Author(s):  
Hemei Wang
Keyword(s):  
Environmental Factors ◽  
Emotion Recognition ◽  
Personal Development ◽  
Social Adaptation ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Deaf People ◽  
Expression Recognition ◽  
Emotional Recognition ◽  
Recognition Ability

<p>Facial emotion recognition plays an important role in daily communication and plays a key role in social adaptation and personal development. Emotion recognition ability is an important component of non-verbal communication system, and it is also a necessary skill to successfully adapt to and deal with environmental problems. A large number of studies have examined the differences in facial expression recognition between deaf and hearing people. Research has proved that deaf people face emotion recognition has different degrees of difficulty, which is manifested in the deaf people's recognition emotion type accuracy rate and reaction time lower than that of healthy people. Deaf people face emotional recognition difficulties are affected by physiological and social environmental factors. Based on these physiological and environmental factors, helping the deaf improve their facial and emotional recognition ability can help improve their social adaptability.</p>

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.

Real-time facial expression recognition using smoothed deep neural network ensemble

2020 ◽  
Vol 28 (1) ◽  
pp. 97-111
Author(s):  
Nadir Kamel Benamara ◽  
Mikel Val-Calvo ◽  
Jose Ramón Álvarez-Sánchez ◽  
Alejandro Díaz-Morcillo ◽  
Jose Manuel Ferrández-Vicente ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Facial Emotion Recognition ◽  
Training Data ◽  
Facial Emotion ◽  
Processing Unit ◽  
Expression Recognition ◽  
Neural Network Ensemble

Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives the model to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.

Impaired recognition of happy facial expressions in bipolar disorder

2014 ◽  
Vol 26 (4) ◽  
pp. 253-259 ◽  
Author(s):  
Linette Lawlor-Savage ◽  
Scott R. Sponheim ◽  
Vina M. Goghari
Keyword(s):  
Bipolar Disorder ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Processing Speed ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Recognition Ability ◽  
Recognition Condition ◽  
Bipolar Patients

BackgroundThe ability to accurately judge facial expressions is important in social interactions. Individuals with bipolar disorder have been found to be impaired in emotion recognition; however, the specifics of the impairment are unclear. This study investigated whether facial emotion recognition difficulties in bipolar disorder reflect general cognitive, or emotion-specific, impairments. Impairment in the recognition of particular emotions and the role of processing speed in facial emotion recognition were also investigated.MethodsClinically stable bipolar patients (n = 17) and healthy controls (n = 50) judged five facial expressions in two presentation types, time-limited and self-paced. An age recognition condition was used as an experimental control.ResultsBipolar patients’ overall facial recognition ability was unimpaired. However, patients’ specific ability to judge happy expressions under time constraints was impaired.ConclusionsFindings suggest a deficit in happy emotion recognition impacted by processing speed. Given the limited sample size, further investigation with a larger patient sample is warranted.

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