  Identity recognition plays an important role in ensuring security in our
daily life. Biometric-based (especially activity-based) approaches are favored
due to their fidelity, universality, and resilience. However, most existing
machine learning-based approaches rely on a traditional workflow where models
are usually trained once for all, with limited involvement from end-users in
the process and neglecting the dynamic nature of the learning process. This
makes the models static and can not be updated in time, which usually leads to
high false positive or false negative. Thus, in practice, an expert is desired
to assist with providing high-quality observations and interpretation of model
outputs. It is expedient to combine both advantages of human experts and the
computational capability of computers to create a tight-coupling incremental
learning process for better performance. In this study, we develop RLTIR, an
interactive identity recognition approach based on reinforcement learning, to
adjust the identification model by human guidance. We first build a base
tree-structured identity recognition model. And an expert is introduced in the
model for giving feedback upon model outputs. Then, the model is updated
according to strategies that are automatically learned under a designated
reinforcement learning framework. To the best of our knowledge, it is the very
first attempt to combine human expert knowledge with model learning in the area
of identity recognition. The experimental results show that the reinforced
interactive identity recognition framework outperforms baseline methods with
regard to recognition accuracy and robustness.
