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

by   Meshia Cédric Oveneke, et al.

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 1.18 20 cattle identities.


Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings

Mode collapse is a well-known issue with Generative Adversarial Networks...

One-shot lip-based biometric authentication: extending behavioral features with authentication phrase information

Lip-based biometric authentication (LBBA) is an authentication method ba...

Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

Due to its convenience, biometric authentication, especial face authenti...

On the Adversarial Inversion of Deep Biometric Representations

Biometric authentication service providers often claim that it is not po...

Effect of Auditory Stimuli on Electroencephalography-based Authentication

Opposed to standard authentication methods based on credentials, biometr...

Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection

The adoption of advanced deep learning (DL) architecture in stuttering d...

MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models

Pre-trained models (PM) have achieved promising results in content gener...

Please sign up or login with your details

Forgot password? Click here to reset