RLTIR: Activity-based Interactive Person Identification based on Reinforcement Learning Tree

03/20/2021
by   Qingyang Li, et al.
0

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.

READ FULL TEXT

page 1

page 11

research
06/22/2018

Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning

Humans are able to understand and perform complex tasks by strategically...
research
09/26/2021

Prioritized Experience-based Reinforcement Learning with Human Guidance: Methdology and Application to Autonomous Driving

Reinforcement learning requires skillful definition and remarkable compu...
research
09/21/2020

Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning

Reinforcement learning is an approach used by intelligent agents to auto...
research
05/22/2020

Reinforcement learning with human advice. A survey

In this paper, we provide an overview of the existing methods for integr...
research
02/04/2021

Persistent Rule-based Interactive Reinforcement Learning

Interactive reinforcement learning has allowed speeding up the learning ...
research
02/18/2023

On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge

Human activity recognition (HAR) has been a classic research problem. In...
research
02/15/2021

Seeing by haptic glance: reinforcement learning-based 3D object Recognition

Human is able to conduct 3D recognition by a limited number of haptic co...

Please sign up or login with your details

Forgot password? Click here to reset