  Many face recognition systems boost the performance using deep learning
models, but only a few researches go into the mechanisms for dealing with
online registration. Although we can obtain discriminative facial features
through the state-of-the-art deep model training, how to decide the best
threshold for practical use remains a challenge. We develop a technique of
adaptive threshold mechanism to improve the recognition accuracy. We also
design a face recognition system along with the registering procedure to handle
online registration. Furthermore, we introduce a new evaluation protocol to
better evaluate the performance of an algorithm for real-world scenarios. Under
our proposed protocol, our method can achieve a 22\% accuracy improvement on
the LFW dataset.
