Automated recognition of facial expressions and gender in humans implemented on mobile devices

2021 ◽  
Author(s):  
Romulus-Cristian Moraru ◽  
Angel Cataron
Keyword(s):  
Mobile Devices ◽  
Facial Expressions ◽  
Automated Recognition ◽  
Recognition Of Facial Expressions ◽  
And Gender

scholarly journals Recognition of facial expressions of emotions in schizophrenia

2013 ◽  
Vol 44 (2) ◽  
pp. 232-238
Author(s):  
Władysław Łosiak ◽  
Joanna Siedlecka
Keyword(s):  
Social Functioning ◽  
Facial Expressions ◽  
Negative Emotions ◽  
Affective Reactions ◽  
Recognition Of Facial Expressions ◽  
Facial Expressions Of Emotion ◽  
Schizophrenic Patients ◽  
And Gender

Abstract Deficits in recognition of facial expressions of emotions are considered to be an important factor explaining impairments in social functioning and affective reactions of schizophrenic patients. Many studies confirmed such deficits while controversies remained concerning the emotion valence and modality. The aim of the study was to explore the process of recognizing facial expressions of emotion in the group of schizophrenic patients by analyzing the role of emotion valence, modality and gender of the model. Results of the group of 35 patients and 35 matched controls indicate that while schizophrenic patients show general impairment in recognizing facial expressions of both positive and the majority of negative emotions, there are differences in deficits for particular emotions. Expressions also appeared to be more ambiguous for the patients while variables connected with gender were found less significant.

scholarly journals Towards Machine Recognition of Facial Expressions of Pain in Horses

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1643
Author(s):  
Pia Haubro Andersen ◽  
Sofia Broomé ◽  
Maheen Rashid ◽  
Johan Lundblad ◽  
Katrina Ask ◽  
...  
Keyword(s):  
Machine Learning ◽  
Facial Expressions ◽  
Experimental Pain ◽  
Ground Truth ◽  
Video Data ◽  
Coding System ◽  
Automated Recognition ◽  
Facial Action ◽  
Recognition Of Facial Expressions ◽  
Pain Recognition

Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for facial activity (Facial Action Coding System), where data are analyzed for pain expressions after coding using machine learning principles. We have devised tools that can aid manual labeling by identifying the faces and facial keypoints of horses. This approach provides promising results in the automated recognition of facial action units from images. The second approach, recurrent neural network end-to-end learning, requires less extraction of features and representations from the video but instead depends on large volumes of video data with ground truth. Our preliminary results suggest clearly that dynamics are important for pain recognition and show that combinations of recurrent neural networks can classify experimental pain in a small number of horses better than human raters.

Emotion Recognition of Facial Expressions Presented in Profile

2021 ◽  
pp. 003329412110184
Author(s):  
Paola Surcinelli ◽  
Federica Andrei ◽  
Ornella Montebarocci ◽  
Silvana Grandi
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Response Times ◽  
Recognition Accuracy ◽  
Recognition Of Facial Expressions ◽  
Profile View ◽  
The Face ◽  
Within Subjects ◽  
Face Stimuli ◽  
Affective Information

Aim of the research The literature on emotion recognition from facial expressions shows significant differences in recognition ability depending on the proposed stimulus. Indeed, affective information is not distributed uniformly in the face and recent studies showed the importance of the mouth and the eye regions for a correct recognition. However, previous studies used mainly facial expressions presented frontally and studies which used facial expressions in profile view used a between-subjects design or children faces as stimuli. The present research aims to investigate differences in emotion recognition between faces presented in frontal and in profile views by using a within subjects experimental design. Method The sample comprised 132 Italian university students (88 female, Mage = 24.27 years, SD = 5.89). Face stimuli displayed both frontally and in profile were selected from the KDEF set. Two emotion-specific recognition accuracy scores, viz., frontal and in profile, were computed from the average of correct responses for each emotional expression. In addition, viewing times and response times (RT) were registered. Results Frontally presented facial expressions of fear, anger, and sadness were significantly better recognized than facial expressions of the same emotions in profile while no differences were found in the recognition of the other emotions. Longer viewing times were also found when faces expressing fear and anger were presented in profile. In the present study, an impairment in recognition accuracy was observed only for those emotions which rely mostly on the eye regions.

SonicFace

2021 ◽  
Vol 5 (4) ◽  
pp. 1-33
Author(s):  
Yang Gao ◽  
Yincheng Jin ◽  
Seokmin Choi ◽  
Jiyang Li ◽  
Junjie Pan ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Microphone Array ◽  
Wearable Sensors ◽  
Real Life ◽  
Privacy Concerns ◽  
Fine Grained ◽  
Recognition Of Facial Expressions ◽  
Ubiquitous Sensing ◽  
Contact Free

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.

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