# FacEDiM: A Face Embedding Distribution Model for Few-Shot Biometric Authentication of Cattle

Source: [FacEDiM: A Face Embedding Distribution Model for Few-Shot Biometric Authentication of Cattle](<https://www.sota2.com/research/paper/68fa70271e209131cea496ee>)

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

Data updated: 2025-10-26T01:35:15.802Z

Authors: Meshia C\\'edric Oveneke, Rucha Vaishampayan, Deogratias Lukamba Nsadisa, Jenny Ambukiyenyi Onya

Published: 2023-02-28T18:28:35.000Z

## Abstract

This work proposes to solve the problem of few-shot biometric authentication by computing the Mahalanobis distance between testing embeddings and a multivariate Gaussian distribution of training embeddings obtained using pre-trained CNNs. Experimental results show that models pre-trained on the ImageNet dataset significantly outperform models pre-trained on human faces. With a VGG16 model, we obtain a FRR of 1.25% for a FAR of 1.18% on a dataset of 20 cattle identities.

## 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/2302.14831>)

[PDF](<https://arxiv.org/pdf/2302.14831.pdf>)