Facial Image Feature Analysis and its Specialization for Fr\'echet Distance and Neighborhoods
<|reference_start|>Facial Image Feature Analysis and its Specialization for Fr\'echet 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 Fr\'echet 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 Fr\'echet distance and image neighborhoods, supported by extensive experiments and in-depth user studies.<|reference_end|>
@article{cetin2024facial,
    title={Facial Image Feature Analysis and its Specialization for Fr\'echet
  Distance and Neighborhoods},
    author={Doruk Cetin, Benedikt Schesch, Petar Stamenkovic, Niko Benjamin Huber,
  Fabio Z"und, Majed El Helou},
    journal={arXiv preprint arXiv:2406.18430},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18430},
    primaryClass={cs.CV}
}