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.

翻译：本研究提出通过计算测试嵌入与使用预训练CNN获取的训练嵌入的多元高斯分布之间的马氏距离，来解决少样本生物特征认证问题。实验结果表明，在ImageNet数据集上预训练的模型显著优于在人脸数据上预训练的模型。采用VGG16模型时，在包含20个牛身份标识的数据集上，当误识率为1.18%时，拒真率可达1.25%。