Face verification is the task of comparing a candidate face to another, and verifying whether it is a match. It is a one-to-one mapping: you have to check if this person is the correct one.
( Image credit: Pose-Robust Face Recognition via Deep Residual Equivariant Mapping )
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The experiments on various classification applications, including handwritten digit recognition, lung nodule classification, face verification and face recognition, demonstrate that the proposed approach is promising to effectively deal with the problem of learning on the data with different quality and leads to the significant and stable improvements in the classification accuracy.
In this paper, we aim to address the problem of heterogeneous or cross-spectral face recognition using machine learning to synthesize visual spectrum face from infrared images.
Accordingly, we address the challenge of developing a lightweight face recognition network of just a few megabytes that can operate with sufficient accuracy in comparison to much larger models.
Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions.
We introduce Set Distribution Networks (SDNs), a novel framework that learns to autoencode and freely generate sets.
The MMA regularization is simple, efficient, and effective.
Our architecture uses a contrastive loss termand a disentangled generative model to sample negative pairs.
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
It is challenging in learning a makeup-invariant face verification model, due to (1) insufficient makeup/non-makeup face training pairs, (2) the lack of diverse makeup faces, and (3) the significant appearance changes caused by cosmetics.
Face verification is a fast-growing authentication tool for everyday systems, such as smartphones.