Soft biometrics using clothing attributes for human identification

Abstract

Recently, soft biometrics has emerged as a novel attribute-based person description for identification. It is likely that soft biometrics can be deployed where other biometrics cannot, and have stronger invariance properties than traditional vision-based biometrics, such as invariance to illumination and contrast. Previously, a variety of soft body and face biometrics have been used for identifying people and have increasingly garnered more research interest and are often considered as major cues for identity, especially in the absence of valid traditional hard biometrics, as in surveillance.

Describing a person by their clothing properties is a natural task performed by people. As yet, clothing descriptions have attracted little attention for biometric purposes as it has been considered unlikely to be a potential cue to identity. There has been some usage of clothing attributes to augment biometric description, but a detailed description has yet to be used. In everyday life, several cases and incidents arise highlighting the usefulness and capability of information deduced from clothing regarding identity. Clothing is inherently more effective for short-term identification, since people can change clothes.

This thesis introduces semantic clothing attributes as a new form of soft biometrics. The usability and efficacy of a novel set of proposed soft clothing traits is explored, showing how they can be exploited for human identification and re-identification purposes. Furthermore, the viability of these traits is investigated in correctly retrieving a subject of interest, given a verbal description of their clothing. The capability of clothing information is further examined in more realistic scenarios offering viewpoint invariant subject retrieval.

Although clothing traits can be naturally described or compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This thesis proposes further a novel set of automatic clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way, we can explore the capability of clothing attributes inferred by human vis-a-vis those which are inferred automatically by computer-vision.

Extended analysis of clothing information is conducted. Human identification and retrieval are achieved, evaluated, and compared using different proposed forms of soft clothing biometrics in addition and in isolation. The experimental results of identification and retrieval highlight clothing attributes as a potentially valuable addition to the field of soft biometrics.

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Identifiers

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

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