Identifying biometrics in the wild – A time, erosion and neural inspired framework for gait identification

Abstract

This paper evaluates the performance of contemporary gait identification systems. A time, erosion and neural inspired framework (TEN-FE) for gait identification was proposed to augment the performance of gait identification systems. Performance of TEN-FE framework was evaluated using CASIA and OU-ISIR large population dataset. Proposed framework relies on CNN and Reinforcement Learning to restrict the impact of confounding factors like baggage and bulky clothing on the accuracy of gait identification systems. Difference in gait signature due to time was also considered and normalized. The results observed a clear increase in system’s performance with minimal complexity and least hardware requirements.