mlsanityData to 14 Oct 2026

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

arXiv 2302.14831 ↗First posted 28 Feb 2023cs.CV cs.LG cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 28 Feb 2023.

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References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Densely Connected Convolutional Networkscited by 6,720
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  6. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  7. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594

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