Deep Metric Learning with Dynamic Margin Hard Sampling Loss

In recent years, with the advent of large data sets and the adoption of end-to-end learning methods using convolutional networks, it is possible to verify and recognize faces on a large-scale dataset effectively, which has even surpassed that of humans. One of the most effective methods is the metric learning with triplet loss. However, when using a large-scale dataset to train the model, the number of the possible triplets will be explosive. Therefore, 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. It is a simplification of the traditional triplet loss function, but it has not degraded the performance of the model. Our method is evaluated on LFW and YTF datasets, which are most widely used benchmarks. The experimental results indicate that the proposed method can outperform some previous methods, and obtain the state-of-art performance with less time.