Monte Carlo Human Identification Refinement Using Joints Uncertainty

In this work, we propose a new method to re-identify the same individual among different people using RGB-D data. Each human signature is a combination of soft biometric traits. In particular, we extract a color-based descriptor and a local feature descriptor through a Monte Carlo-based algorithm taking into account the uncertainty of human joints and, applied to each descriptor, refines the similarity match against a spatiotemporal database that updates over time. We analyzed the effects of Monte Carlo refinement in terms of the final maximum matching score obtained for the two descriptors. In addition, we tested the performance of the proposed method on a widely used public dataset against one of the best re-identification methods in the literature. Our method achieves an average recognition rate of 99.1 % rank-1 without identification error. Its robustness also makes it suitable for industrial applications.