This study examined how racism and racial tensions potentially threatened the face needs of Black and White interracial couples. Specifically, this study investigated the benefit of family approval of one's interracial relationship. Couples use of corrective face work in response to prejudice was also examined. This research used a qualitative, interpretive method to gather and analyze data from 14 personal interviews. The interview schedule enabled interviewees to use storytelling to share their experience of being in a Black and White interracial relationship. Interviewees were asked questions surrounding their experience as the partner in an interracial relationship. Couples shared how they told their family that their romantic partner was a different race and shared how they encountered prejudice when they are out in public. Further, they spoke about how people stare at them and make comments about their interracial relationship. The data underwent a thematic analysis (Owen, 1984) where I reviewed the data and searched for themes that were recurrent and repeated by interviewees. There were several themes that emerged. First, the very presence of racial tensions and racism affects interracial couples. Second, family support seemed to mitigate threats to couples' positive and negative faces and enabled couples to engage in open and honest dialogue with their family and their significant other. Finally, even couples with family support, engaged in corrective face work to respond to the face threats posed by racism and racial tensions.

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The last 15 years have seen a significant progress in the development of general purpose algorithms and software for polyhedral computation. Many polytopes of practical interest have enormous output complexity and are often highly degenerate, posing severe difficulties for known general purpose algorithms. They are, however, highly structured and attention has turned to exploiting this structure, particularly symmetry. We focus on polytopes arising from combinatorial optimization problems. In particular, we study the face lattice of the metric polytope associated with the well-known maxcut and multicommodity flow problems, as well as with finite metric spaces. Exploiting the high degree of symmetry, we provide the first complete orbitwise description of the higher layers of the face lattice of the metric polytope for any dimension. Further computational and combinatorial issues are presented. / Thesis / Master of Applied Science (MASc)

Thesis advisor: Stefano Anzellotti / There has been much debate over how the functional organization of vision develops. Contemporary theories that are inspired by analyzing neural data with machine learning models have led to new insights in understanding brain organization. Given the evolutionary importance of face perception and the specialized mechanisms that have evolved to support evaluating it, examining faces offers a unique way to study a dedicated mechanism that shares much of its organization in ventral and lateral neural pathways with other social stimuli, and provide insight into a more general principle of the organization of social perception. According to a classical view of face perception (Bruce and Young, 1986; Haxby, Hoffman, and Gobbini, 2000), face identity and facial expression recognition are performed by separate neural substrates (ventral and lateral temporal face-selective regions, respectively). However, recent studies challenge this view, showing that expression valence can also be decoded from ventral regions (Skerry and Saxe, 2014; Li, Richardson, and Ghuman, 2019) and identity from lateral regions (Anzellotti and Caramazza, 2017). These recent findings have inspired the formulation of an alternative hypothesis. From a computational perspective, it may be possible to process face identity and facial expression jointly by disentangling information for the two properties. This hypothesis was tested using deep convolutional neural network (DCNN) models as a proof of principle. Subsequently, this is then followed by evaluating the representational content of static face stimuli within ventral and lateral temporal face- selective regions using intracranial electroencephalography (iEEG). This is then extended to investigating the representation content of dynamic faces within these regions using functional magnetic resonance imaging (fMRI). The results reported here as well as the reviewed literature may help to support the reevaluation of the roles the ventral and lateral temporal neural pathways play in processing socially-relevant stimuli. / Thesis (PhD) — Boston College, 2024. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology and Neuroscience.

Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analysis, and linear discriminant analysis are few other statistical techniques that are commonly used in solving the face recognition problem. Genetic algorithm, elastic bunch graph matching, artificial neural network, etc. are few of the techniques that have been proposed and implemented. The objective of this thesis paper is to provide insight into different methods available for face recognition, and explore methods that provided an efficient and feasible solution. Factors affecting the result of face recognition and the preprocessing steps that eliminate such abnormalities are also discussed briefly. Principal Component Analysis (PCA) is the most efficient and reliable method known for at least past eight years. Elastic bunch graph matching (EBGM) technique is one of the promising techniques that we studied in this thesis work. We also found better results with EBGM method than PCA in the current thesis paper. We recommend use of a hybrid technique involving the EBGM algorithm to obtain better results. Though, the EBGM method took a long time to train and generate distance measures for the given gallery images compared to PCA. But, we obtained better cumulative match score (CMS) results for the EBGM in comparison to the PCA method. Other promising techniques that can be explored separately in other paper include Genetic algorithm based methods, Mixture of principal components, and Gabor wavelet techniques.

In eyewitness identification cases, suspect misidentification is the leading factor attributed to wrongful convictions (Scheck, Neufeld, & Dwyer, 2000), thus, it is of applied importance to identify factors that contribute to the false recollection of faces. One potential factor addressed in the current study was whether face memory and subsequent identification for other-race-faces is biased by the degree to which a target face posses facial features associated with ethnic identity. Individual differences in level of processing (global, local) and prejudice were tested as potential mechanisms contributing to biased judgments. In Experiment 1 a standard face recognition task revealed that prejudice, level of processing, and face-type interacted to predict recognition bias. In Experiment 2 results showed that positive misidentifications (i.e., choosing an incorrect foil) were more likely when a stereotypical versus non-stereotypical Black actor was witnessed committing the crime. Results are discussed in terms of theoretical and practical implications.

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Thesis (Ph.D.)--McMaster University, 2007. / Supervisor: D. Maurer. Includes bibliographical references (p. 177-193).

Thesis (M.A.)--Brandeis University, 2009. / Title from PDF title page (viewed on May 29, 2009). Includes bibliographical references.