Holistic methods using CNNs and margin-based losses have dominated research
on face recognition. In this work, we depart from this setting in two ways: (a)
we employ the Vision Transformer as an architecture for training a very strong
baseline for face recognition, simply called fViT, which already surpasses most
state-of-the-art face recognition methods. (b) Secondly, we capitalize on the
Transformer's inherent property to process information (visual tokens)
extracted from irregular grids to devise a pipeline for face recognition which
is reminiscent of part-based face recognition methods. Our pipeline, called
part fViT, simply comprises a lightweight network to predict the coordinates of
facial landmarks followed by the Vision Transformer operating on patches
extracted from the predicted landmarks, and it is trained end-to-end with no
landmark supervision. By learning to extract discriminative patches, our
part-based Transformer further boosts the accuracy of our Vision Transformer
baseline achieving state-of-the-art accuracy on several face recognition
benchmarks.

本文提出了一种全新的脸部识别方法 —— 使用 Vision Transformer 作为架构进行培训，称为 fViT，并通过简单的网络预测面部标记点的坐标再进行后续处理，最终达到了在几个面部识别基准上的最佳准确度。

基于视觉变换器的面部局部识别

Part-based Face Recognition with Vision Transformers

The goal of this paper is to discover a set of discriminative patches which
can serve as a fully unsupervised mid-level visual representation. The desired
patches need to satisfy two requirements: 1) to be representative, they need to
occur frequently enough in the visual world; 2) to be discriminative, they need
to be different enough from the rest of the visual world. The patches could
correspond to parts, objects, "visual phrases", etc. but are not restricted to
be any one of them. We pose this as an unsupervised discriminative clustering
problem on a huge dataset of image patches. We use an iterative procedure which
alternates between clustering and training discriminative classifiers, while
applying careful cross-validation at each step to prevent overfitting. The
paper experimentally demonstrates the effectiveness of discriminative patches
as an unsupervised mid-level visual representation, suggesting that it could be
used in place of visual words for many tasks. Furthermore, discriminative
patches can also be used in a supervised regime, such as scene classification,
where they demonstrate state-of-the-art performance on the MIT Indoor-67
dataset.

本文旨在发现一组具有判别性的补丁，用作完全无监督的中级视觉表示，通过对图像补丁数据集进行无监督聚类和训练判别分类器来证明其有效性，进而能用于许多任务中代替视觉单词，并且在 MIT Indoor-67 数据集上展示了其在场景分类等任务中的最新性能。