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

Meshia Cédric Oveneke, Rucha Vaishampayan, Deogratias Lukamba Nsadisa, Jenny Ambukiyenyi Onya
2/28/2023
cs.CVcs.LGcs.NE

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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.

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Cite this paper

@article{oveneke2023facedim,
  title  = {FacEDiM: A Face Embedding Distribution Model for Few-Shot Biometric Authentication of Cattle},
  author = {Meshia Cédric Oveneke and Rucha Vaishampayan and Deogratias Lukamba Nsadisa and Jenny Ambukiyenyi Onya},
  year   = {2023},
  eprint = {2302.14831},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.14831v2}
}

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