Towards a Face Recognition Model Analyzer

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Abstract:

Machine learning allows computers to learn a model for a given task, such as face recognition, with a high degree of accuracy, using data. However, after these models are generated, they are often treated as black boxes by developers and the limitations of a model are often unknown to end-users. To address these issues, this paper introduces the Face Recognition Model Analyzer (FRMA) ontology and a semantically enabled Resultset viewer. Together these resources describe image features relevant to face recognition and allow users to explore how well a face recognition model does at classifying images that contain an image feature. We evaluated the ontology and Resultset viewer by loading in the Labeled Faces in the Wild [1] dataset, enriching the images with image tags [2], and exploring two popular face recognition models, Facenet [3] and DLib [4]. Using the FRMA ontology and the Result-set viewer, we discovered several classic face recognition model limitations, such as trouble classifying images with occlusions. This evaluation shows that these resources can discover model limitations which can make face recognition model reuse easier for future users.

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Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
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