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Soft biometrics enable human description and identification from low-quality surveillance footage. This study 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. The authors annotate 100 subject images to provide a coherent, in-depth appraisal of the collected annotations...
Automatically describing pedestrians in surveillance footage is crucial to facilitate human accessible solutions for suspect identification. We aim to identify pedestrians based solely on human description, by automatically retrieving semantic attributes from surveillance images, alleviating exhaustive label annotation. This work unites a deep learning solution with relative soft biometric labels,...
Categorising gender for soft biometric recognition is especially challenging from low quality surveillance footage. Our novel approach discovers super fine-grained visual taxonomies of gender from pairwise similarity comparisons, annotated via crowdsourcing. This paper presents our techniques for collection, interpretation and clustering of perceived visual similarities, and discusses the transition...
Soft biometrics are human describable, distinguishing human characteristics. We present a baseline solution to the problem of identifying individuals solely from human descriptions, by automatically retrieving soft biometric labels from images. Probe images are then identified from a gallery of known soft biometric signatures, using their predicted labels. We investigate four labelling techniques...
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...
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