The Use of Semantic Human Description as a Soft Biometric

Gait as a biometric has a unique advantage that it can be used when images are acquired at a distance and other biometrics are at too low a resolution to be perceived. In such a situation, there is still information which can be readily perceived by human vision, yet is difficult to extract automatically. We examine how this information can be used to enrich the recognition process. We call these descriptions semantic annotations and investigate their use in biometric scenarios. We outline a group of visually assessable physical traits formulated as a mutually exclusive set of semantic terms; we contend that these traits are usable in soft biometric fusion. An experiment to gather semantic annotations was performed and the most reliable traits are identified using ANOVA. We rate the ability to correctly identify subjects using these semantically prescribed traits, both in isolation as well as in fusion with an automatically derived gait signature.