Soft biometric recognition from comparative crowdsourced annotations

Soft biometrics provide cues that enable human identification from low quality video surveillance footage. This paper discusses a new crowdsourced dataset, collecting comparative soft biometric annotations from a rich set of human annotators. We now include gender as a comparative trait, and find comparative labels are more objective and obtain more accurate measurements than previous categorical labels. Using our pragmatic dataset, we perform semantic recognition by inferring relative biometric signatures. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The experiment is guaranteed to return the correct match in the the top 7% of results with 10 comparisons, or top 13% of results using just 5 sets of subject comparisons.