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).

The Return of the Repressed: Recovering the Public Face of U.S. Sociology, One Hundred Years On

Abstract: At its inception one hundred years ago, the American Sociological Society was concerned to separate itself from the social movements and reform impulses to which it had hitherto been connected. One hundred years of professionalism have achieved the splitting and amassed an impressive body of theory, empirical analysis, and innovative techniques, but sociology’s public face has remained underdeveloped. Public sociology today requires rethinking the meaning of the public sphere as well as that of the discipline of sociology. Moreover, in today’s world, reaching out to publics demands recognition of the interests of the different social sciences within the new global dispensation.

A Face Concern Approach to Conflict Management - A Malaysian Perspective

Abstract: This study presents a review of conflict management from a face concern approach. It presents the various definitions on conflict and types of conflict. It further looks at how face negotiation theory explains the use of various conflict management styles in individualist and collectivist cultures. In addition, it provides some insight into conflict management from a Malaysian perspective.

Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

Abstract: Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little work considers how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches.

Face in Chinese, Japanese, and U.S. American cultures.

Abstract: This paper delved into the nature of face as a socio-psycho-linguistic concept through the examination of its varying conceptualizations and components in three different cultures: Chinese, Japanese, and U.S. American. The points of investigation are as follows: (1) semantic fields of the terms which signify ‘face’ in the Chinese, Japanese, and English languages, (2) cultures, social behaviors, and social systems of the three peoples, (3) differences between face and other similar concepts, such as prestige and honor, (4) comparative weights of attributes comprising face, such as formal position, personal reputation, conformity, and integrity of social being, which may vary significantly according to culture.