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.
翻译:以生物计量为基础的方法(特别是基于活动的方法)由于其忠诚性、普遍性和复原力而得到偏好。然而,大多数现有的机器学习方法都依赖于传统的工作流程,即模型通常得到一次性培训,最终使用者对过程的参与有限,忽视了学习过程的动态性质。这使得模型静止,无法及时更新,通常导致高假正或假负,因此在实践中,希望专家协助提供高质量的观测和对模型产出的解释。适宜于将人类专家的优势和计算机的计算能力结合起来,以创造紧密结合的渐进式学习进程,提高绩效。在这项研究中,我们开发了基于强化学习的互动式身份识别方法RLTIR,以调整学习过程的动态性质。我们首先建立了一个基本树型身份识别模型,通常导致高正或假正反。然后,模型根据在指定的强化学习框架内自动学习的战略加以更新。我们开发了一种基于强化学习过程的渐进式学习过程。我们开发了一个基于强化学习过程的互动身份识别方法,从而通过人文指导来调整身份识别模式。我们首先在模型中引入了一名专家,然后根据在指定的强化学习框架下自动学习的战略加以更新,然后,然后,然后将最佳的准确性识别模型的识别模型与最强化的识别模型的确认。