Multi Model Approach to Extract Human Features in Real Time

Face detection and analysis systems has been growing in last few years for various applications. Since the hardware performance increase in last few years, useof Deep Learning, Convolution Neural Network, Face detection, Face analysis techniques is increasing and day by day developed models are breaking accuracies of previous models and research in various tasks. Facial analysis system with age, gender and emotion recognition have been proposed with good accuracies for real-time and non-real time both. The present research paper focuses to provide a robust system architecture for age, gender and emotion recognition in real time which can be use in commercial, healthcare, and many more industries. To achieve this a literature survey is done on the same topic with previous researches to compare their results. The final model architecture proposed in this research paper is efficient and fast and provides accurate results as compare to previous researches

Are byline biases an issue of the past? The effect of author’s gender and emotion norm prescriptions on the evaluation of news articles on gender equality

When female journalists write about issues of gender equality, they often become the target of incivility and their work is devaluated. Research has investigated such devaluations based on journalists’ gender under the scope of byline biases, analysing if it matters to readers whether a news piece is authored by a male or female journalist. In this paper, we set out to study if gender byline biases occur when journalists write about gender equality. As gender attributions become particularly salient through the presentation of gendered emotion norms, we also inquire in how it matters for readers’ interest in reading such an article and the attributed credibility of the author when an article prescribes gender-specific emotions. We report findings from two consecutive experimental studies, manipulating gender bylines and emotion norm prescriptions and include reader gender as a quasi-experimental factor. Our findings show that gender byline biases against female authors are depending on content and context characteristics and only become activated when gender cues are clearly visible. At the same time, we found a tendency to judge female authors as more credible for topics on gender equality, which (partly) mitigated negative effects on reading intention for female authors. The prescription of emotion norms did not further strengthen biases against female authors. Our study opens the path for further investigations into the question when gender bylines are activated and underlines the challenges for female journalists’ visibility when they address controversial issues such as gender equality.

Are women the more empathetic gender? The effects of gender role expectations

The present research aimed to extend the state of knowledge regarding the relationship between self-perceived empathy and traditional gender roles and placed particular focus on the contextual conditions under which gender differences in empathy are present, can be created, or eliminated. Across two studies, women rated themselves higher in empathy than men in all experimental conditions, whereas an objective female superiority in emotion recognition was only evident in one condition. In Study 1 (n = 736), using the term ‘social-analytic capacity’ instead of ‘empathic capacity’ increased gender differences in self-reported empathy and resulted in women performing better in the Eyes-test than men. In a neutral task (verbal intelligence), gender differences (in this case, a male superiority), were only found when participants believed that this task had an association with empathy. In Study 2 (n = 701), gender differences in self-reported empathic capacity, but not in performance in emotion recognition, increased when motivation for empathy was raised. Further, gender-role orientation mediated the association between gender and self-reported empathic capacity, whereas it did not account for the association between gender and emotion recognition. Overall, the present studies provide strong support for the idea that empathy is influenced by contextual factors and can be systematically biased by gender roles and stereotypical beliefs.

Gender and emotion in exile: Evelyn Scott and Cyril Kay-Scott’s experience in Brazil (1914-1918)

Historicamente o exílio tem representado parte significativa da vida de escritores, artistas e intelectuais em toda parte. No Brasil, esse movimento migratório ocorreu sobretudo no período das duas grandes guerras. Foi o caso, por exemplo, da escritora americana Evelyn Scott que, no final de 1913, fugiu com Cyril Kay-Scott, um renomado médico e pesquisador, à época, casado, pai de quatro filhos e com mais que o dobro de sua idade. Sem passaportes, levando pouquíssimo dinheiro no bolso, o casal fugiu primeiro para Nova York, em seguida para Londres e finalmente para o Brasil, onde enfrentaram pobreza, fome e isolamento profundo. A experiência foi particularmente dolorosa para Evelyn Scott, que chegara ao Brasil grávida e teve que enfrentar um parto difícil na cidade de Natal (RN). O objetivo deste trabalho é investigar o discurso autobiográfico de Evelyn Scott e Cyril Kay-Scott sobre o longo exílio auto-imposto que viveram no Brasil entre os anos 1914 e 1919. A análise se baseia nas autobiografias Escapade, de Evelyn Scott, e Life is too short, de Cyril Kay-Scott. Evidências indicam que gênero e emoção são aspectos importantes que revelam diferenças não apenas de estilo como também de visões de mundo.

Saudi Gender Emotional Expressions in Using Instagram

There are plentiful studies exploring gender emotional differences. Gender and emotion stereotypes make people believe that there are certain emotions associated with each gender and this is supported by many studies. The purpose of this research is to analyze the emotional expressions of Saudi men and women in Instagram, a social networking service. This paper aims to explore the Saudi differences of emotional expressions. Also, if gender emotion stereotypes apply on these expressions or not. Data is collected through corpus analysis of Arabic comments for a certain post on Instagram. The results of this study demonstrate that there are differences in Saudis' expressions of emotions in which each gender uses different expressions. Additionally, gender stereotypes of emotions are applied to their emotional expressions that is men express negative emotions more while women express positive emotions. Another result is that women are found to be more emotional than men. Overall, the findings contribute to increase understanding of online emotional expressions of both Saudi genders.