Efficient Identification in Large-Scale Vein Recognition Systems Using Spectral Minutiae Representations
Benedikt-Alexander Mokroß, Pawel Drozdowski, Christian Rathgeb, Christoph Busch · Advances in computer vision and pattern recognition · 2019
Large biometric systems, e.g. the Indian AADHAAR project, regularly perform millions of identification and/or de-duplication queries every day, thus yielding an immense computational workload. Dealing with this challenge by merely upscaling the hardware resources is often insufficient, as it quickly reaches limits in terms of purchase and operational costs. Therefore, it is additionally important for the underlying systems software to implement lookup strategies with efficient algorithms and data structures. Due to certain properties of biometric data (i.e. fuzziness), the typical workload reduction methods, such as traditional indexingIndexing, are unsuitable; consequently, new and specifically tailored approaches must be developed for biometric systems. While this is a somewhat mature research field for several biometric characteristics (e.g. fingerprint and iris), much fewer works exist for vascular characteristics. In this chapter, a survey of the current state of the art in vascular identification is presented, followed by introducing a vein indexingIndexing method based on proven concepts adapted from other biometric characteristics (specifically spectral minutiae representationSpectral Minutiae Representation (SMR) and BloomBloom filter filter-based indexing). Subsequently, a benchmark in an open-set identification scenario is performed and evaluated. The discussion focuses on biometric performance, computational workload, and facilitating parallel, SIMD and GPU computation.