McNeely-White, David G., authorBeveridge, J. Ross, advisorBlanchard, Nathaniel, committee memberKirby, Michael, committee memberPeterson, Chris, committee member2022-08-292022-08-292022https://hdl.handle.net/10217/235674Deep convolutional neural networks trained for face recognition are found to output face embeddings which share a fundamental structure. More specifically, one face verification model's embeddings (i.e. last--layer activations) can be compared directly to another model's embeddings after only a rotation or linear transformation, with little performance penalty. If only rotation is required to convert the bulk of embeddings between models, there is a strong sense in which those models are learning the same thing. In the most recent experiments, the structural similarity (and dissimilarity) of face embeddings is analyzed as a means of understanding face recognition bias. Bias has been identified in many face recognition models, often analyzed using distance measures between pairs of faces. By representing groups of faces as groups, and comparing them as groups, this shared embedding structure can be further understood. Specifically, demographic-specific subspaces are represented as points on a Grassmann manifold. Across 10 models, the geodesic distances between those points are expressive of demographic differences. By comparing how different groups of people are represented in the structure of embedding space, and how those structures vary with model designs, a new perspective on both representational similarity and face recognition bias is offered.born digitaldoctoral dissertationsengface recognitionrepresentational similarityGrassmannbias in face recognitionTextThis material is open access and distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 United States License. (https://creativecommons.org/licenses/by-nc-nd/4.0).