Facial Image Feature Analysis and its Specialization for Fréchet Distance and Neighborhoods

Assessing distances between images and image datasets is a fundamental task in vision-based research. It is a challenging open problem in the literature and despite the criticism it receives, the most ubiquitous method remains the Fréchet Inception Distance. The Inception network is trained on a spe...

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Bibliographic Details
Published inarXiv.org
Main Authors Cetin, Doruk, Schesch, Benedikt, Stamenkovic, Petar, Huber, Niko Benjamin, Zünd, Fabio, Majed El Helou
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.06.2024
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Datasets
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Summary:Assessing distances between images and image datasets is a fundamental task in vision-based research. It is a challenging open problem in the literature and despite the criticism it receives, the most ubiquitous method remains the Fréchet Inception Distance. The Inception network is trained on a specific labeled dataset, ImageNet, which has caused the core of its criticism in the most recent research. Improvements were shown by moving to self-supervision learning over ImageNet, leaving the training data domain as an open question. We make that last leap and provide the first analysis on domain-specific feature training and its effects on feature distance, on the widely-researched facial image domain. We provide our findings and insights on this domain specialization for Fréchet distance and image neighborhoods, supported by extensive experiments and in-depth user studies.
ISSN:2331-8422