Adaptive Security for Human Surveillance Using Multimodal Open Set Biometric Recognition

In most human surveillance and forensic applications, key requirement is often to achieve highest possible true positive identification accuracy with a judicious compromise in accepting false positive identities. However with such scenario's in mind, there has been lack of any effort to develop adaptive security management for open set biometric recognition and most of the available prior work in literature has been focused on performance improvement for the rank-one recognition. This paper investigates the multimodal open set biometric recognition to address the conflicting requirements between the offered identification rate and high false positive identification rate while admitting possible unknown subjects/suspects in the higher rank (more than rank-one) list. The proposed approach attempts to offer accurate open set rank-K recognition which can automatically select a decision threshold to the desired/requested security level using ant colony optimization and provide a useful solution to a range of dynamic security problems in surveillance and high security applications. The performance evaluation of the proposed framework is ascertained through rigorous experimentation on three multimodal matchers from publicly available NIST BSSRlandXM2VTS databases.