# Facial Image Feature Analysis and its Specialization for Fr'echet Distance and Neighborhoods

arXiv: https://arxiv.org/abs/2406.18430 · HTML: https://mlsanity.com/paper/2406.18430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2024.

## Abstract (arXiv)

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.