Canonical Face Embeddings

by   David McNeely-White, et al.

We present evidence that many common convolutional neural networks (CNNs) trained for face verification learn functions that are nearly equivalent under rotation. More specifically, we demonstrate that one face verification model's embeddings (i.e. last–layer activations) can be compared directly to another model's embeddings after only a rotation or linear transformation, with little performance penalty. This finding is demonstrated using IJB-C 1:1 verification across the combinations of ten modern off-the-shelf CNN-based face verification models which vary in training dataset, CNN architecture, way of using angular loss, or some combination of the 3, and achieve a mean true accept rate of 0.96 at a false accept rate of 0.01. When instead evaluating embeddings generated from two CNNs, where one CNN's embeddings are mapped with a linear transformation, the mean true accept rate drops to 0.95 using the same verification paradigm. Restricting these linear maps to only perform rotation produces a mean true accept rate of 0.91. These mappings' existence suggests that a common representation is learned by models with variation in training or structure. A discovery such as this likely has broad implications, and we provide an application in which face embeddings can be de-anonymized using a limited number of samples.



There are no comments yet.


page 5

page 6

page 7

page 9


Common CNN-based Face Embedding Spaces are (Almost) Equivalent

CNNs are the dominant method for creating face embeddings for recognitio...

MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices

In this paper, we present a class of extremely efficient CNN models call...

L2-constrained Softmax Loss for Discriminative Face Verification

In recent years, the performance of face verification systems has signif...

Federated Learning of User Verification Models Without Sharing Embeddings

We consider the problem of training User Verification (UV) models in fed...

Analyzing deep CNN-based utterance embeddings for acoustic model adaptation

We explore why deep convolutional neural networks (CNNs) with small two-...

Towards Distortion-Predictable Embedding of Neural Networks

Current research in Computer Vision has shown that Convolutional Neural ...

Angular Visual Hardness

Although convolutional neural networks (CNNs) are inspired by the mechan...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.