In recent years, the performance of face verification systems has
significantly improved using deep convolutional neural networks (DCNNs). A
typical pipeline for face verification includes training a deep network for
subject classification with softmax loss, using the penultimate layer output as
the feature descriptor, and generating a cosine similarity score given a pair
of face images. The softmax loss function does not optimize the features to
have higher similarity score for positive pairs and lower similarity score for
negative pairs, which leads to a performance gap. In this paper, we add an
L2-constraint to the feature descriptors which restricts them to lie on a
hypersphere of a fixed radius. This module can be easily implemented using
existing deep learning frameworks. We show that integrating this simple step in
the training pipeline significantly boosts the performance of face
verification. Specifically, we achieve state-of-the-art results on the
challenging IJB-A dataset, achieving True Accept Rate of 0.909 at False Accept
Rate 0.0001 on the face verification protocol. Additionally, we achieve
state-of-the-art performance on LFW dataset with an accuracy of 99.78%, and
competing performance on YTF dataset with accuracy of 96.08%.

本研究中，我们给特征描述符添加了一个 L2 约束，以保证它们位于一个固定半径的超球面上，该步骤可以显著提高面部验证的性能，特别是在 IJB-A 数据集上实现了 0.909 的 True Accept Rate 以及 0.0001 的 False Accept Rate。