  Person re-identification is a critical security task for recognizing a person
across spatially disjoint sensors. Previous work can be computationally
intensive and is mainly based on low-level cues extracted from RGB data and
implemented on a PC for a fixed sensor network (such as traditional CCTV). We
present a practical and efficient framework for mobile devices (such as smart
phones and robots) where high-level semantic soft biometrics are extracted from
RGB and depth data. By combining these cues, our approach attempts to provide
robustness to noise, illumination, and minor variations in clothing. This
mobile approach may be particularly useful for the identification of persons in
areas ill-served by fixed sensors or for tasks where the sensor position and
direction need to dynamically adapt to a target. Results on the BIWI dataset
are preliminary but encouraging. Further evaluation and demonstration of the
system will be available on our website.
