Emotion Recognition Model Based on Facial Expressions, Ethnicity and Gender Using Backpropagation Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Nabil M. Hewahi ◽  
AbdulRahman M. Baraka
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Expression Recognition ◽  
Backpropagation Neural Network ◽  
Backpropagation Algorithm ◽  
Improvement Rate ◽  
Race And Gender ◽  
And Gender ◽  
Ethnicity And Gender ◽  
Positive Effect

Many emotion recognition approaches are built using facial expressions, but few of them use both the ethnicity and gender as attributes. The authors have developed an approach based on Artificial Neural Networks (ANN) using backpropagation algorithm to recognize the human emotion through facial expressions, ethnicity and gender. Their approach has been tested by using MSDEF dataset, and found that there is a positive effect on the accuracy of the recognition of emotion if they use both the ethnic group and gender as inputs to the system. Although this effect is not significant, but considerable (Improvement rate reached 8%). The authors also found that females have more accurate emotion expression recognition than males and found that the gender increases the accuracy of emotion recognition. Regardless of the used dataset, the authors’ approach obtained better results than some research on emotion recognition. This could be due to various reasons such as the type of the selected features and consideration of race and gender.

Age, Race and Gender Issues Related to On-line Learning

2010 ◽  
pp. 999-1012
Author(s):  
M. F. Stuck ◽  
Mary. C. Ware
Keyword(s):  
Learning Style ◽  
Learning Technology ◽  
Race And Gender ◽  
Blended Courses ◽  
Mobile Learning Technology ◽  
On Line ◽  
And Gender ◽  
Skills Learning ◽  
On Line Learning ◽  
Ethnicity And Gender

Research has shown that demographic factors such as age, race, ethnicity and gender affect one’s communication skills, learning style preference, and consequently, one’s preferences for aspects of on-line learning. This chapter will explore the literature related to these issues (i.e., age, race, gender) as they affect students’ preferences for and success with various styles of on-line learning (e.g., distance learning, hybrid or blended courses, mobile learning technology).

scholarly journals A quasi-experimental study of ethnic and gender bias in university grading

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254422
Author(s):  
Carina Saxlund Bischoff ◽  
Anders Ejrnæs ◽  
Olivier Rubin
Keyword(s):  
Quantitative Methods ◽  
Ethnic Discrimination ◽  
Race And Gender ◽  
Interaction Terms ◽  
Quasi Experimental ◽  
Discrimination In Employment ◽  
Gender Based ◽  
And Gender ◽  
Written Assignments ◽  
Ethnicity And Gender

This paper contributes to the debate on race- and gender-based discrimination in grading. We apply a quasi-experimental research design exploiting a shift from open grading in 2018 (examinee’s name clearly visible on written assignments), to blind grading in 2019 (only student ID number visible). The analysis thus informs name-based stereotyping and discrimination, where student ethnicity and gender are derived from their names on written assignments. The case is a quantitative methods exam at Roskilde University (Denmark). We rely on OLS regression models with interaction terms to analyze whether blind grading has any impact on the relative grading differences between the sexes (female vs. male examinees) and/or between the two core ethnic groups (ethnic minorities vs. ethnic majority examinees). The results show no evidence of gender or ethnic bias based on names in the grading process. The results were validated by several checks for robustness. We argue that the weaker evidence of ethnic discrimination in grading vis-à-vis discrimination in employment and housing suggests the relevance of gauging the stakes involved in potentially discriminatory activities.

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 EDUCATIONAL ATTAINMENT AND YOUNG ADULT HEALTH IN THE UNITED STATES: DIFFERENCES BY RACE AND GENDER

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S591-S591
Author(s):  
Grace A Noppert
Keyword(s):  
Health Outcome ◽  
The United States ◽  
Adult Health ◽  
Race And Gender ◽  
Logistic Regression Models ◽  
And Gender ◽  
Using Data ◽  
Young Adult Health ◽  
Ethnicity And Gender ◽  
The Relationship

Abstract There is compelling evidence to suggest that educational disparities in health differ by both race and gender. This study examines the relationship between respondents’ education and six health outcomes related to cardiometabolic and inflammatory outcomes using data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health (ages 24-32 years; N = 13,458). We used logistic regression models to examine the relationship between education and the odds of each health outcome. Models were stratified by race and gender. We found that the association between education and each health outcome differed by race/ethnicity and gender. While among whites we observed an association between education and each health outcome, for blacks we observed no such associations. It may be that the benefits of education are particularly salient for those in more structurally advantaged positions, pointing to the continued need to address structural inequalities by both gender and race.

scholarly journals Two-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition

2020 ◽  
Vol 24 (6) ◽  
pp. 792-801
Author(s):  
Chang Liu ◽  
◽  
Kaoru Hirota ◽  
Bo Wang ◽  
Yaping Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Affective State ◽  
Texture Feature ◽  
Service Robots ◽  
Facial Features ◽  
Expression Recognition

An emotion recognition framework based on a two-channel convolutional neural network (CNN) is proposed to detect the affective state of humans through facial expressions. The framework consists of three parts, i.e., the frontal face detection module, the feature extraction module, and the classification module. The feature extraction module contains two channels: one is for raw face images and the other is for texture feature images. The local binary pattern (LBP) images are utilized for texture feature extraction to enrich facial features and improve the network performance. The attention mechanism is adopted in both CNN feature extraction channels to highlight the features that are related to facial expressions. Moreover, arcface loss function is integrated into the proposed network to increase the inter-class distance and decrease the inner-class distance of facial features. The experiments conducted on the two public databases, FER2013 and CK+, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 72.56% and 94.24%, respectively. The improvement in emotion recognition accuracy makes our approach applicable to service robots.

scholarly journals Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network

2011 ◽  
Vol 5 (1) ◽  
Author(s):  
Zaenal Abidin ◽  
Agus Harjoko
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
False Positive ◽  
Facial Expression Recognition ◽  
Type I ◽  
Expression Recognition ◽  
Backpropagation Neural Network ◽  
Neural Network Approach ◽  
Image Type

Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15,  for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.Keywords— facial expression, fisherface method, PCA, LDA, backpropagation neural network.

Teaching the Veil: Race, Ethnicity, and Gender in Classical Theory Courses

2020 ◽  
pp. 233264922092189
Author(s):  
Victoria Reyes ◽  
Karin A. C. Johnson
Keyword(s):  
Social Theory ◽  
Classical Theory ◽  
Pedagogical Tools ◽  
Race And Gender ◽  
Du Bois ◽  
Essay Exams ◽  
Race Ethnicity ◽  
The Veil ◽  
And Gender ◽  
Ethnicity And Gender

By documenting the erasure of W.E.B. Du Bois’s scientific contributions to sociology, Aldon Morris’s The Scholar Denied was a catalyst for scholars to rethink how we teach and understand social theory and a call to recognize the racialized origins of our discipline. How can we incorporate these insights into our teaching beyond a token addition of Du Bois to classical theory courses? Drawing on comments from anonymous student evaluations and completed assignments including essay exams, final papers, and end-of-year reflections from one classical theory course, the authors argue that teaching classical theory requires teaching about race, ethnicity, and gender and outline three pedagogical principles. First, we assert that it starts with the syllabus. Second, we demonstrate how incorporating theorists’ biographies situates them in their sociohistorical contexts. Finally, active learning observational assignments reveal how research is a scholarly conversation and demonstrate the enduring importance, and limitations, of classical theories and theorists. Together, these pedagogical tools show how the classical theory canon is racialized. By providing conceptual and logistical tools scholar-teachers can use to incorporate race, ethnicity, and gender in classical theory courses, we highlight how issues of race and gender should not be relegated to substantive courses. Instead, they are central to understanding and teaching the foundations of sociology.

scholarly journals A Review on Facial Emotion Recognition Using Machine and Deep Learning Algorithm

2020 ◽  
pp. 09-13
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Learning Algorithm ◽  
Facial Feature ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Expression Recognition

Facial emotions are the changes in facial expressions about a person’s inner excited tempers, objectives, or social exchanges which are scrutinized with the aid of computer structures that attempt to subsequently inspect and identify the facial feature and movement variations from visual data. Facial emotion recognition (FER) is a noteworthy area in the arena of computer vision and artificial intelligence due to its significant commercial and academic potential. FER has become a widespread concept of deep learning and offers more fields for application in our day-to-day life. Facial expression recognition (FER) has gathered widespread consideration recently as facial expressions are thought of as the fastest medium for communicating any of any sort of information. Recognizing facial expressions provides an improved understanding of a person’s thoughts or views. With the latest improvement in computer vision and machine learning, it is plausible to identify emotions from images. Analyzing them with the presently emerging deep learning methods enhance the accuracy rate tremendously as compared to the traditional contemporary systems. This paper emphases the review of a few of the machine learning, deep learning, and transfer learning techniques used by several researchers that flagged the means to advance the classification accurateness of the FEM.

Geographies of Campus Inequality

2020 ◽  
Author(s):  
Janel E. Benson ◽  
Elizabeth M. Lee
Keyword(s):  
First Generation ◽  
College Campuses ◽  
College Aspirations ◽  
First Generation Students ◽  
Generation Status ◽  
Race And Gender ◽  
Selective College ◽  
Race Ethnicity ◽  
And Gender ◽  
Ethnicity And Gender

In efforts to improve equity, selective college campuses are increasingly focused on recruiting and retaining first-generation students—those whose parents have not graduated from college. In Geographies of Campus Inequality, sociologists Benson and Lee argue that these approaches may fall short if they fail to consider the complex ways first-generation status intersects with race, ethnicity, and gender. Drawing on interview and survey data from selective campuses, the authors show that first generation students do not share a universal experience. Rather, first generation students occupy one of four disparate geographies on campus within which they negotiate academic responsibilities, build relationships, engage in campus life, and develop post-college aspirations. Importantly, the authors demonstrate how geographies are shaped by organizational practices and campus constructions of class, race, and gender. Geographies of Campus Inequality expands the understanding of first-generation students’ campus lives and opportunities for mobility by showing there is more than one way to be first generation.

Promise for Whom? “Free-College” Programs and Enrollments by Race and Gender Classifications at Public, 2-Year Colleges

2020 ◽  
Vol 42 (4) ◽  
pp. 603-627
Author(s):  
Denisa Gándara ◽  
Amy Li
Keyword(s):  
Hispanic Students ◽  
The United States ◽  
Full Time ◽  
Race And Gender ◽  
Demographic Groups ◽  
College Programs ◽  
And Gender ◽  
Ethnicity And Gender ◽  
First Time ◽  
Postsecondary Attainment

Promise programs are proliferating across the United States, with wide variation in their design. Using national data on 33 Promise programs affecting single, 2-year colleges, this study examines program effects on first-time, full-time college enrollments of students by race/ethnicity and gender classification. Results suggest Promise programs are associated with large percent increases in enrollments of Black and Hispanic students, especially students classified as females, at eligible colleges. Promise programs with merit requirements are associated with higher enrollment of White and Asian, Native Hawaiian, or Pacific Islander female students; those with income requirements are negatively associated with enrollment of most demographic groups. More generous Promise programs are associated with greater enrollment increases among demographic groups with historically higher levels of postsecondary attainment.

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