Video-Based Framework for Face Recognition in Video
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Biometric systems have evolved significantly over the past years: from single-sample fully-controlled verification matchers to a wide range of multi-sample multi-modal fully-automated person recognition systems working in a diverse range of unconstrained environments and behaviors. The methodology for biometric system evaluation however has remained practically unchanged, still being largely limited to reporting false match and non-match rates only and the trade-off curves based thereon. Such methodology may no longer be sufficient and appropriate for investigating the performance of state-of-the-art systems. This paper addresses this gap by establishing taxonomy of biometric systems and proposing a baseline methodology that can be applied to the majority of contemporary biometric systems to obtain an all-inclusive description of their performance. In doing that, a novel concept of multi-order performance analysis is introduced and the results obtained from a large-scale iris biometric system examination are presented.