Comparative face soft biometrics for human identification

The recent growth in CCTV systems and the challenges of automatically identifying humans under the adverse visual conditions of surveillance have increased the interest in soft biometrics, which are physical attributes that can be used to describe people semantically. Soft biometrics enable human identification based on verbal descriptions, and they can be captured in conditions where it is impossible to acquire traditional biometrics such as iris and fingerprint. The research on facial soft biometrics has tended to focus on identification using categorical attributes, whereas comparative attributes have shown a better accuracy. Nevertheless, the research in comparative facial soft biometrics has been limited to small constrained databases, while identification in surveillance systems involves unconstrained large databases. In this chapter, we explore human identification through comparative facial soft biometrics in large unconstrained databases using the Labelled Faces in the Wild (LFW) database. We propose a novel set of attributes and investigate their significance. Also, we analyse the reliability of comparative facial soft biometrics for realistic databases and explore identification and verification using comparative facial soft biometrics. The results of the performance analysis show that by comparing an unknown subject to a line up of ten subjects only; a correct match will be found in the top 2.08% retrieved subjects from a database of 4038 subjects