Abstract
This paper describes an application of the Flash algorithm [3] to scalable fingerprint-based identification (one-to-many searches). Flash is a high-dimensional indexing algorithm akin to Geometric Hashing [11] that has been used for similarity searching in a number of other domains, including model based object recognition [3], genomic sequences homology detection [4], and 3D flexible molecular matching and docking [16].
A large number of independent properties of each model fingerprint that are invariant under Euclidean transformations are extracted and stored in a look-up table. Match candidates and their pose transformation parameters are then formed by indexing in this table, using identical invariants generated from a query fingerprint. This has the desirable properties of determining appropriate match candidates without having to compare the query to each individual model fingerprint in the database. Candidate hypothesis evidence is then gathered in a parameter space with an approach similar to the Generalized Hough Transform [2].
Results of this preliminary implementation on a database of 100,000 models show good scalability properties. Measured False Positive and False Negative Rates allow this approach to be extended to databases with tens of millions of fingerprints. Reported performance measurements show an equivalent 1 to 1 matching rate of about 150,000 prints/sec. on an 8-way SMP PowerPC workstation or, equivalently, on an 8-node SP/2 platform.
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Califano, A., Germain, B., Colville, S. (1997). A high-dimensional indexing scheme for scalable fingerprint-based identification. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_101
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DOI: https://doi.org/10.1007/3-540-63930-6_101
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