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

Source: [Facial Image Feature Analysis and its Specialization for Fr\\'echet Distance and Neighborhoods](<https://www.sota2.com/research/paper/68fa73d41e209131cea5e5e1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:07.495Z

Authors: Doruk Cetin, Benedikt Schesch, Petar Stamenkovic, Niko Benjamin Huber, Fabio Z\\"und, Majed El Helou

Published: 2024-06-26T15:27:26.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.18430>)

[PDF](<https://arxiv.org/pdf/2406.18430.pdf>)