University of Southampton , School of Electronics and Computer Science ,
Gait and face biometrics have a unique advantage in that they can be used when images are acquired at a distance and signals are at too low a resolution to be perceived by other biometrics. Given such situations, some traits can be difficult to extract automatically but can still be perceived semantically using human vision. It is contended that such semantic annotations are usable as soft biometric signatures, useful for identification tasks. Feature subset selection techniques are employed to compare the distinguishing ability of individual semantically described physical traits. Their identification ability is also explored, both in isolation and in the improvement of the recognition rates of some associated gait biometric signatures using fusion techniques.
This is the first approach to explore semantic descriptions of physiological human traits as used alone or to complement primary biometric techniques to facilitate recognition and analysis of surveillance video. Potential traits to be described are explored and justified against their psychological and practical merits. A novel dataset of semantic annotations is gathered describing subjects in two existing biometric datasets. Two applications of these semantic features and their associated biometric signatures are explored using the data gathered. We also draw on our experiments as a whole to highlight those traits thought to be most useful in assisting biometric recognition overall.
Effective analysis of surveillance data by humans relies on semantic retrieval of the data which has been enriched by semantic annotations. A manual annotation process is time-consuming and prone to error due to various factors. We explore the semantic content-based retrieval of surveillance captured subjects. Working under the premise that similarity of the chosen biometric signature implies similarity of certain semantic traits, a set of semantic retrieval experiments are performed using well established Latent Semantic Analysis techniques.
Actions (login required)