What's in a crowd? Analysis of face-to-face behavioral networks

The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing important differences as well as striking similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks

A Cost-Benefit Analysis of Face-to-Face and Virtual Communication: Overcoming the Challenges

Virtual communication has become the norm for many organizations (Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002; Bergiel, Bergiel, & Balsmeier, 2008; Hertel, Geister, & Konradt, 2005). As technology has evolved, time and distance barriers have dissolved, allowing for access to experts worldwide. The reality of business today demands the use of virtual communication for at least some work, and many professionals will sit on a virtual team at some point (Dewar, 2006). Although virtual communication offers many advantages, it is not without challenges. This article examines the costs and benefits associated with virtual and face-to-face communication, and identifies strategies to overcome virtual communication\u27s challenges

Theories of identity and the analysis of face

This paper explores the insights that theories of identity can offer for the conceptualisation and analysis of face. It argues that linguists will benefit from taking a multidisciplinary approach, and that by drawing on theory and research in other disciplines, especially in social psychology, they will gain a clearer and deeper understanding of face. The paper starts by examining selected theories of identity, focusing in particular on Simon's (2004) self-respect model of identity and Brewer and Gardner's (1996) theory of levels of identity. Key features from these theories are then applied to the conceptualisation and analysis of face. With the help of authentic examples, the paper demonstrates how inclusion of these multiple perspectives can offer a richer and more comprehensive understanding of face and the frameworks needed for analysing it

Face analysis using curve edge maps

This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking

Tied factor analysis for face recognition across large pose differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches

Frontal Facial Symmetry Detection Using Eigenvalue Method

Facial symmetry is correspondence of face components on the both sides of face, left and right of a dividing line or about a center or an axis. Most of the research use face component like eyes, nose and ears component to identify facial symmetry. In this paper we suggest to add mouth as another face component to increase accuracy in facial symmetry detection. The results of facial symmetry detection are used for authentication process, analysis in medical, psychology and anthropology scope. By using MATLAB 7.1 we develop a program that can analyze face,asymmetry or not with utilizing eigenvalue. The contribution of this analysis is to know whether eigenvalue is suitable or not in analyzing facial symmetry