Analysing comparative soft biometrics from crowdsourced annotations

Soft biometrics enable human description and identification from low quality surveillance footage. This paper premises the design, collection and analysis of a novel crowdsourced dataset of comparative soft biometric body annotations, obtained from a richly diverse set of human annotators. We annotate 100 subject images to provide a coherent, in-depth appraisal of the collected annotations and inferred relative labels. The dataset includes gender as a comparative trait and we find that comparative labels characteristically contain additional discriminative information over traditional categorical annotations. Using our pragmatic dataset, we perform semantic recognition by inferring relative biometric signatures using a RankSVM algorithm. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The approach can reliably 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.