Discovering human descriptions for ubiquitous visual identification

Abstract

Identifying suspects in surveillance footage is paramount in ensuring public safety, preventing crime, policing and forensic investigation. At present, finding an individual in real-world CCTV footage given only an eye-witness description is near impossible. The vast majority of contemporary research assumes coarse, expertly-defined categories to describe subjects, ineffective in dealing with unconstrained, low quality and obscured images. Such brittle representations hamper semantic image discrimination and the ability to learn robust predictors from challenging subject matter. This thesis explores human and machine centric techniques for representing and learning semantic human descriptions for suspect identification. By investigating the duality of human-machine communication, we enhance the capabilities of traditional attributes and soft biometric descriptors, expanding their versatility and applicability towards challenging images and large-scale surveillance datasets. We experiment with crowdsourcing human annotations using ordered and similarity comparisons, and estimating attributes from images employing a variety of state-of-the-art machine learning techniques. Our focus is on utilising a lean lexicon of global and body characteristics that are most pertinent when estimated from stand-alone surveillance footage. Significant improvements in suspect retrieval and identification performance are achieved by discovering enhanced soft biometric descriptions which represent visual trait characteristics with more precision and relevance. This work evolves the areas of soft biometrics and identity science, drawing ideas from contemporary image attribute recognition, semantic attribute discovery, pedestrian reidentification and perceptual psychology. Our findings indicate that increasing not only the volume, but the complexity of information conveyed between humans and machines is key in deploying soft biometrics ubiquitously.

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ORCID for Mark Nixon: orcid.org/0000-0002-9174-5934

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