Face (sociological concept)

About: Face (sociological concept) is a research topic. Over the lifetime, 5171 publications have been published within this topic receiving 96109 citations. The topic is also known as: Lose face & Face (sociological concept).

A Novel and Efficient Method to Detect the Face Coverings to Ensurethe Safety using Comparison Analysis

Abstract: A face mask worn by a single person or a group of individuals can be identified using face mask detection, an AI-based system that examines a video stream. A confidence value is produced for each detection by our DeepSight programme. Either a person is labeled as "wearing a mask" or "not wearing a mask" depends on their classification. The World Health Organization (WHO) recommends that a face mask completely cover the face, including the chin and nose. Face Mask Detection System may be used with both CCTV cameras and already-installed USB or IP cameras. To distinguish between persons wearing masks and those who aren’t, it employs the most recent computer vision techniques. Compact model’s accuracy is really great, and it can tell if you’re wearing a mask properly or not (i.e.; mask covering the nose). Face mask detection data may be used to a wide range of settings and sectors, including corporate offices, shops, public transit, airports, and taxis. The main motive of the paper by the author is represented as solving the problem using cnn and transfer learning and also a comparison between both the approaches.

College Students with Children are Common and Face Many Challenges in Completing Higher Education

Abstract: Nearly 25 percent of college students in the U.S., or four million students, have dependent children. Among low-income and first-generation college students, more than a third are parents, and students of color are especially likely to be balancing parenting and college, with 37% of African American, 33% of Native American, and 25 percent of Latino students raising children. Being a parent substantially increases the likelihood of leaving college with no degree, with 53% of parents vs. 31% of nonparents having left with no degree after six years. Among low-income college students with children, parents are 25% less likely to obtain a degree than low-income adults without children. Student parents operate under often crushing time demands, with more than 40% working full time or more, and over half spending 30 hours per week on care-giving activities. Even in the face of these pressures, students with children, like other students who are older than average, have higher GPA's than non-parents.

Stereotypes and the discursive accomplishment of intergroup differentiation: Talking about ‘the other’ in a global business organization

Abstract: This article analyzes how employees in a global business organization talk about their colleagues in other countries. Employees were asked to discuss their work practices in focus group settings, and give examples of how they experience ‘the other’. Using Discursive Psychology and Politeness Theory as the analytic approaches, the article analyzes pieces of discourse to disclose social psychological phenomena such as group identity, intergroup differentiation, and stereotypes. The analyses show that talking about ‘the other’ is potentially face-threatening, and mitigating discourse features are used repeatedly to soften the criticism. We also see how uncovering stereotypes is a mutual accomplishment in the group, and how group members gradually move from relatively innocent to blatantly negative outgroup stereotypes. The analyses also show that participants engage in meta-reflections on the nature of stereotypes, which may serve as another mitigating device, and that talk about ‘the other’ is used to create intergroup differentiation. Finally, the article discusses the implications of these findings for cross-cultural communication and work practices in organizations.

DBCFace: Towards Pure Convolutional Neural Network Face Detection

Abstract: Face detection generally requires prior boxes and an extra non-maximum suppression(NMS) post-processing in modern deep learning methods. However, anchor design and anchor matching strategy significantly affect the performance of face detectors, so we have to spend a lot of time on anchor designing for different business scenarios. The other issue is that NMS cannot be easily parallelized and it may become a bottleneck of detection speed. In this paper, we propose a simple yet efficient pure convolutional neural network face detection method, named dual-branch center face detector(DBCFace for short), which solve face detection via a dual branch fully convolutional framework without extra anchor design and NMS. Extensive experiments are conducted on four popular face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE, demonstrating that our method is comparable with state-of-the-art methods while the speed is faster.