scholarly journals Emotion Detection of Human Face

2019 ◽  
Vol 9 (1) ◽  
pp. 5521-5524 ◽  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Language Translation ◽  
Online Marketing ◽  
Facial Emotion ◽  
Good Resolution ◽  
Human Beings ◽  
Emotion Detection ◽  
Unsupervised Neural Network ◽  
User Device

Facial emotion analysis is the basic idea to train the system to understand the different facial expressions of human beings. The Facial expressions are recorded by the use of camera which is attached to user device. Additionally this project will be helpful for the online marketing of the products as it can detect the facial expressions and sentiment of the person. It is the study of people sentiment, opinions and emotions. Sentiment analysis is the method by which information is taken from the facial expressions of people in regard to different situations. The main aim is to read the facial expressions of the human beings using a good resolution camera so that the machine can identify the human sentiments. Convolutional neural network is used as an existing system which is unsupervised neural network to replace that with a supervised mechanism which is called supervised neural network. It can be used in gaming sector, unlock smart phones, automated facial language translation etc.

scholarly journals Facial Emotion Recognition for Autism Children

2020 ◽  
Vol 9 (7) ◽  
pp. 1274-1278
Keyword(s):  
Facial Expressions ◽  
Autism Spectrum ◽  
Facial Emotion ◽  
Expression Recognition ◽  
Emotion Detection ◽  
Age And Gender ◽  
Social Media Platforms ◽  
Haar Cascade ◽  
And Gender ◽  
Cascade Method

Nowadays Autism children find it difficult to interact socially with people emotions and make themselves isolated. This paper proposes Emotion detection for Autism spectrum disorder children (ASD). It is self-possessed of python libraries Open CV, Haar-cascade method and Age and gender prediction. Conversely, most existing methods rely on the detection of facial expressions of people in social media platforms such as snapchat use facial recognition technology and also detecting facial emotions from their Facial expressions in image. And for a better involvement of the children’s social behaviour, here a face is captured in real time and age, gender and emotions are predicted by Facial expression recognition (FER). This proposed system helps to improve the Autism children behaviour as they often observe the facial expressions of humans and try to imitate their emotions which make a huge difference in their behaviour.

scholarly journals IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1289
Author(s):  
Navjot Rathour ◽  
Sultan S. Alshamrani ◽  
Rajesh Singh ◽  
Anita Gehlot ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Real Time ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Human Beings ◽  
Emotion Detection ◽  
Resource Constrained

Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).

scholarly journals Real-time facial emotion recognition using lightweight convolution neural network

2021 ◽  
Vol 1827 (1) ◽  
pp. 012130
Author(s):  
Qi Li ◽  
Yun Qing Liu ◽  
Yue Qi Peng ◽  
Cong Liu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Convolution Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Negative parental emotional environment increases the association between childhood behavioral problems and impaired recognition of negative facial expressions

2021 ◽  
pp. 1-10
Author(s):  
Daniel T. Burley ◽  
Christopher W. Hobson ◽  
Dolapo Adegboye ◽  
Katherine H. Shelton ◽  
Stephanie H.M. van Goozen
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Behavioral Problems ◽  
Expressed Emotion ◽  
Interactive Effect ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Environment ◽  
Negative Comments ◽  
Child Behavioral Problems

Abstract Impaired facial emotion recognition is a transdiagnostic risk factor for a range of psychiatric disorders. Childhood behavioral difficulties and parental emotional environment have been independently associated with impaired emotion recognition; however, no study has examined the contribution of these factors in conjunction. We measured recognition of negative (sad, fear, anger), neutral, and happy facial expressions in 135 children aged 5–7 years referred by their teachers for behavioral problems. Parental emotional environment was assessed for parental expressed emotion (EE) – characterized by negative comments, reduced positive comments, low warmth, and negativity towards their child – using the 5-minute speech sample. Child behavioral problems were measured using the teacher-informant Strengths and Difficulties Questionnaire (SDQ). Child behavioral problems and parental EE were independently associated with impaired recognition of negative facial expressions specifically. An interactive effect revealed that the combination of both factors was associated with the greatest risk for impaired recognition of negative faces, and in particular sad facial expressions. No relationships emerged for the identification of happy facial expressions. This study furthers our understanding of multidimensional processes associated with the development of facial emotion recognition and supports the importance of early interventions that target this domain.

scholarly journals A Review of the Effects of Valenced Odors on Face Perception and Evaluation

i-Perception ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 204166952110095
Author(s):  
Elmeri Syrjänen ◽  
Håkan Fischer ◽  
Marco Tullio Liuzza ◽  
Torun Lindholm ◽  
Jonas K. Olofsson
Keyword(s):  
Individual Differences ◽  
Facial Expressions ◽  
Curve Analysis ◽  
Facial Emotion ◽  
Future Research ◽  
Emotion Classification ◽  
Open Research ◽  
Classification Tasks ◽  
Odor Exposure ◽  
Z Curve

How do valenced odors affect the perception and evaluation of facial expressions? We reviewed 25 studies published from 1989 to 2020 on cross-modal behavioral effects of odors on the perception of faces. The results indicate that odors may influence facial evaluations and classifications in several ways. Faces are rated as more arousing during simultaneous odor exposure, and the rated valence of faces is affected in the direction of the odor valence. For facial classification tasks, in general, valenced odors, whether pleasant or unpleasant, decrease facial emotion classification speed. The evidence for valence congruency effects was inconsistent. Some studies found that exposure to a valenced odor facilitates the processing of a similarly valenced facial expression. The results for facial evaluation were mirrored in classical conditioning studies, as faces conditioned with valenced odors were rated in the direction of the odor valence. However, the evidence of odor effects was inconsistent when the task was to classify faces. Furthermore, using a z-curve analysis, we found clear evidence for publication bias. Our recommendations for future research include greater consideration of individual differences in sensation and cognition, individual differences (e.g., differences in odor sensitivity related to age, gender, or culture), establishing standardized experimental assessments and stimuli, larger study samples, and embracing open research practices.

scholarly journals Effects of early institutionalization on emotion processing in 12-year-old youth

2017 ◽  
Vol 29 (5) ◽  
pp. 1749-1761 ◽  
Author(s):  
Johanna Bick ◽  
Rhiannon Luyster ◽  
Nathan A. Fox ◽  
Charles H. Zeanah ◽  
Charles A. Nelson
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Positive Impact ◽  
Developmental Trajectories ◽  
Emotion Processing ◽  
Institutional Care ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Emotional Facial Expressions ◽  
Fearful Faces

AbstractWe examined facial emotion recognition in 12-year-olds in a longitudinally followed sample of children with and without exposure to early life psychosocial deprivation (institutional care). Half of the institutionally reared children were randomized into foster care homes during the first years of life. Facial emotion recognition was examined in a behavioral task using morphed images. This same task had been administered when children were 8 years old. Neutral facial expressions were morphed with happy, sad, angry, and fearful emotional facial expressions, and children were asked to identify the emotion of each face, which varied in intensity. Consistent with our previous report, we show that some areas of emotion processing, involving the recognition of happy and fearful faces, are affected by early deprivation, whereas other areas, involving the recognition of sad and angry faces, appear to be unaffected. We also show that early intervention can have a lasting positive impact, normalizing developmental trajectories of processing negative emotions (fear) into the late childhood/preadolescent period.

Sign in / Sign up

Export Citation Format

Share Document