Latent Fingerprint Indexing - Computer Science and Engineering

slowest module (i.e., the orientation field descriptor). Inthis case, the total time for the indexing is 28,667 referenceprints searched per second, per latent. Given the manuallymarked features, the time needed to extract the additionalfeatures from all the 258 latents is around 3 seconds.6. ConclusionsWe have presented an indexing approach for latent fingerprintsthat outperforms state-of-the-art indexing techniqueson the public domain latent database NIST SD27against a background database of 267,258 rolled prints. Theproposed approach combines various level 1 and level 2fingerprint features. Improvement in the overall indexingaccuracy due to the addition of each individual feature isshown. The experimental results show that filtering not onlyimproves the computational efficiency of latent search, butthe latent matching accuracy is maintained.In the proposed approach, the indexing is applied toall latents in NIST SD27 without considering their quality.As future work, we plan to analyze latent quality sothat a decision can be made upfront as to whether indexingscheme should be applied to that latent. Currently, the fusionscheme assigns the same weight to individual indexingmodules for every latent. Latent examiners usually considerseveral features to make exclusions, e.g. fingerprint type,presence of singular points, etc. However, they do not necessarilyconsider all the features at the same time. A betterfusion scheme would take into account the differences inthe latents so that the weights assigned to different featuresused in indexing can be adaptively determined, or the differentfeatures could be used sequentially as opposed to inparallel as implemented here.References[1] A. M. Bazen and S. H. Gerez. Systematic methods for thecomputation of the directional fields and singular points offingerprints. IEEE Trans. on Pattern Analysis and MachineIntelligence, 24(7):905–919, July 2002.[2] B. Bhanu and X. Tan. Fingerprint indexing based on novelfeatures of minutiae triplets. IEEE Trans. on Pattern Analysisand Machine Intelligence, 25(5):616–622, 2003.[3] R. Cappelli. Fast and accurate fingerprint indexing based onridge orientation and frequency. IEEE Trans. on Systems,Man, and Cybernetics, Part B: Cybernetics, 41(6):1511–1521, December 2011.[4] R. Cappelli and M. Ferrara. A fingerprint retrieval systembased on level 1 and level 2 features. 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