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arXiv · 2302.14831

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

Meshia Cédric Oveneke·Rucha Vaishampayan·Deogratias Lukamba Nsadisa·Jenny Ambukiyenyi Onya
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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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Meshia Cédric Oveneke, Rucha Vaishampayan, Deogratias Lukamba Nsadisa, Jenny Ambukiyenyi Onya. 2023-02-28. FacEDiM: A Face Embedding Distribution Model for Few-Shot Biometric Authentication of Cattle. https://arxiv.org/abs/2302.14831

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