Cross-context Analysis for Long-term View-point Invariant Person Re-identification via Soft-biometrics using Depth Sensor

We propose a novel methodology for cross-context analysis in person re-identification using 3D features acquired from consumer grade depth sensors. Such features, although theoretically invariant to perspective changes, are nevertheless immersed in noise that depends on the view point, mainly due to the low depth resolution of these sensors and imperfections in skeleton reconstruction algorithms. Thus, the re-identification of persons observed on different poses requires the analysis of the features that transfer well its characteristics between view-points. Taking view-point as context, we propose a cross-context methodology to improve the re-identification of persons on different view-points. On the contrary to 2D cross-view re-identification methods, our approach is based on 3D features that do not require an explicit mapping between view-points, but nevertheless take advantage of feature selection methods that improve the re-identification accuracy.