Computational workload in biometric identification systems: an overview <s> Other <s> This correspondence proposes new candidate list reduction criteria for fingerprint indexing approaches. The basic idea is that, given a query fingerprint, the initial set of scores produced by an indexer could contain useful information to reduce the candidate list. Novel reduction criteria have been proposed, and extensive experiments have been carried out over five publicly available benchmarks, using two state-of-the-art fingerprint indexing techniques. Although quite simple, the proposed criteria achieved remarkable results, allowing a substantial reduction of the candidate list: for instance, at 1% error rate, the average penetration rate of a state-of-the-art minutiae-based indexer decreases from 27% to 3.9% on FVC2000 DB2. The new reduction criteria are applicable to any indexing approach, since they only require a list of scores as input. <s> BIB001 </s> Computational workload in biometric identification systems: an overview <s> Other <s> The discriminative power of the iris enables reliable biometric recognition on large-scale databases where a rapid comparison of biometric reference data is essential to limit response times. In case of national-sized databases a one-to-many comparison might still represent a bottleneck of a biometric identification system, in particular if numerous relative tilt angles have to be considered in the comparisons stage. While a compensation of head tilts improves the robustness of an iris recognition system, extensive feature alignment increases the probability of a false match as well as comparison time. In this paper we present a novel method to accelerate iris biometric comparators in an accuracy-preserving way. Emphasis is put on the alignment of iris biometric reference data, i.e. iris-codes. Based on an analysis of the nature of iris-codes and comparison scores between them we propose an efficient two-step alignment process referred to as TripleA. This scheme, which can be operated in various modes, significantly reduces the amount of relative tilt angles to be considered during iris-code comparisons. Hence, comparison time as well as the probability of a false match are reduced at the same time. In an experimental evaluation on the Casia v4-Interval iris database we achieve a more than fourfold speed-up in the comparison stage maintaining biometric performance using different feature extraction techniques. <s> BIB002 </s> Computational workload in biometric identification systems: an overview <s> Other <s> The necessity of biometric template alignment imposes a significant computational load and increases the probability of false positive occurrences in biometric systems. While for some modalities, automatic pre-alignment of biometric samples is utilised, this topic has not yet been explored for systems based on the iris. This paper presents a method for pre-alignment of iris images based on the positions ofautomatically detected eye corners. Existing work in the area of automatic eye corner detection has hitherto only involved visible wavelength images; for the near-infrared images, used in the vast majority of current iris recognition systems, this task is significantly more challenging and as of yet unexplored. A comparative study of two methods for solving this problem is presented in this paper. The eye corners detected by the two methods are then used for the pre-alignment and biometric performance evaluation experiments. The system utilising image pre-alignment is benchmarked against a baseline iris recognition system on the iris subset of the BioSecure database. In the benchmark, the workload associated with alignment compensation is significantly reduced, while the biometric performance remains unchanged or even improves slightly. <s> BIB003 </s> Computational workload in biometric identification systems: an overview <s> Other <s> Iris recognition is used in many applications around the world, with enrollment sizes as large as over one billion persons in India's Aadhaar program. Large enrollment sizes can require special optimizations in order to achieve fast database searches. One such optimization that has been used in some operational scenarios is 1:First search. In this approach, instead of scanning the entire database, the search is terminated when the first sufficiently good match is found. This saves time, but ignores potentially better matches that may exist in the unexamined portion of the enrollments. At least one prominent and successful border-crossing program used this approach for nearly a decade, in order to allow users a fast"token-free"search. Our work investigates the search accuracy of 1:First and compares it to the traditional 1:N search. Several different scenarios are considered trying to emulate real environments as best as possible: a range of enrollment sizes, closed- and open-set configurations, two iris matchers, and different permutations of the galleries. Results confirm the expected accuracy degradation using 1:First search, and also allow us to identify acceptable working parameters where significant search time reduction is achieved, while maintaining accuracy similar to 1:N search. <s> BIB004 </s> Computational workload in biometric identification systems: an overview <s> Other <s> In recent years, morphing of facial images has arisen as an important attack vector on biometric systems. Detection of morphed images has proven challenging for automated systems and human experts alike. Likewise, in recent years, the importance of efficient (fast) biometric identification has been emphasised by the rapid rise and growth of large-scale bio-metric systems around the world.In this paper, the aforementioned, hitherto unrelated, topics within the biometrics domain are combined: the properties of morphed images are exploited for the purpose of improving the transaction times of a biometric identification system. Specifically, morphs of two or more samples are used in the pre-selection step of a two-stage biometric identification system. In a proof-of-concept experimental evaluation using two state-of-the-art open-source facial recognition frameworks it is shown, that the proposed system achieves hit rates comparable to that of an exhaustive search-based baseline, while significantly reducing the penetration rate (and thus the computational workload) associated with the biometric identification transactions. <s> BIB005
This subsection presents computational workload reduction approaches which do not fit into the previous categories. Table 6 summarises the surveyed methods. A simple method of reducing the computational workload in an exhaustive search is performing an early exit strategy, i.e. finishing the search once first (not necessarily best) suitable candidate is found. This is sometimes referred to as "one-tofirst" search. In BIB004 this search strategy is analysed extensively for iris recognition in order to assess potential degradation of biometric performance. It is discovered, that the biometric performance degradation is strongly dependent on the decision thresholds (accuracy target) and size of the enrolment database. For some parameters, the biometric performance of an exhaustive search can be maintained, while the computational workload is significantly reduced. In BIB001 several strategies were proposed, which reduce candidate lists (produced by other methods) through analysis of comparison scores. In BIB002 an approach to reduce the number of relative shifting positions of the Iris-Codes which need to be considered in a template comparison was presented. The method is based on an analysis of the intrinsic properties of the iris data and achieves a considerable speed-up without impairing the biometric performance. In BIB003 a pre-alignment of raw iris images is performed. The method is based on automatic detection of eye corners and several other points in raw iris images, and subsequently aligning the eye corners onto a horizontal line. Thus, at a later point, once features are extracted, fewer relative shifting positions need to be considered during template comparisons. The approach of BIB005 relies on morphing (signallevel fusion). The facial images from the enrolment database are morphed (in 2s, 4s, or 8s), whereby biometric information from multiple subjects is fused into one image. The morphed images are then utilised for pre-filtering (see subsection 3.1). In addition to being explicitly used in some computational workload reduction schemes surveyed in this article, information fusion is an important aspect in ensuring the scalability of biometric systems in terms of biometric performance. For a comprehensive survey of this topic, the reader is referred to e.g.  .