Metric learning with triplet loss is one of the most effective methods for face verification, which aims to minimize the distance of positive pairs while maximizing the distance of negative pairs in feature embedding space. The arduous hard triplets mining and insufficient inter-class and intra-class variations are the two limitations of the previous methods. In this paper, we propose an improved triplet loss based on deep neural network for end-to-end metric learning, which can effectively cut down the number of the possible triplets and increase the proportion of hard triplets. It considers not only the relative distance between positive and negative pairs with the same probe images, but also the absolute distance between positive and negative pairs with different probe images, resulting in a smaller intra-class variation and a larger inter-class variation by adding a new constraint to push away negative pairs from positive pairs. In particular, a dynamic margin is proposed based on the distribution of positive and negative pairs in a batch, which can avoid the under-sampling or over-sampling problems. Our method is evaluated on LFW and YTF datasets, which are the most widely used benchmarks. The experimental results indicate that the proposed method can greatly outperform the standard triplet loss, and obtain the state-of-the-art performance with less time.

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用于人脸验证的具有动态余量硬采样损失的深度度量学习

具有三元组损失的度量学习是最有效的人脸验证方法之一，其目的是在特征嵌入空间中最小化正对的距离，同时最大化负对的距离。困难的三元组挖掘和类间和类内变化不足是以前方法的两个局限性。在本文中，我们提出了一种基于深度神经网络的改进的三元组损失，用于端到端的度量学习，可以有效地减少可能的三元组的数量并增加硬三元组的比例。它不仅考虑了相同探测图像的正负对之间的相对距离，还考虑了不同探测图像的正负对之间的绝对距离，通过添加新的约束来推开正对中的负对，从而导致更小的类内变化和更大的类间变化。特别是，基于批次中正负对的分布提出了动态余量，可以避免欠采样或过采样问题。我们的方法是在 LFW 和 YTF 数据集上进行评估的，它们是使用最广泛的基准。实验结果表明，所提出的方法可以大大优于标准的三元组损失，并在更短的时间内获得最先进的性能。我们的方法是在 LFW 和 YTF 数据集上进行评估的，它们是使用最广泛的基准。实验结果表明，所提出的方法可以大大优于标准的三元组损失，并在更短的时间内获得最先进的性能。我们的方法是在 LFW 和 YTF 数据集上进行评估的，它们是使用最广泛的基准。实验结果表明，所提出的方法可以大大优于标准的三元组损失，并在更短的时间内获得最先进的性能。