Fingerprint Matching Technology The Basics DRS \\ 7jun02 1 techieDetail16.ppt Overview A wide variety of fingerprint matching software and hardware is available AuthenTec sensors can work with most varieties of matching systems including: z AuthenTec supplied matchers z Most independently available matchers Fingerprint matchers are catagorized: z Primarily - by type of data used z Secondarily – by method of comparing that data DRS \\ 7jun02 2 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns z Macro-features – Core, deltas, scars z Classical Ridge Minutia z Generalized Pattern z Specific ridge pattern Fine Structure z Ridge shape – Lateral ridge shape – Vertical ridge shape z z DRS \\ 7jun02 Local curvature Pores (sweat glands) 3 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns z Macro-features – Core, deltas, scars z Classical Ridge Minutia z Generalized Pattern z Specific ridge pattern Fine Structure z Ridge shape – Lateral ridge shape – Vertical ridge shape z z DRS \\ 7jun02 Local curvature Pores (sweat glands) 4 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns z Macro-features – Core, deltas, scars z Classical Ridge Minutia z Generalized Pattern z Specific ridge pattern Fine Structure z Ridge shape – Lateral ridge shape – Vertical ridge shape z z DRS \\ 7jun02 Local curvature Pores (sweat glands) 5 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns z Macro-features – Core, deltas, scars z z z Classical Ridge Minutia Generalized Pattern Specific ridge pattern Fine Structure z Ridge shape – Lateral ridge shape – Vertical ridge shape z z DRS \\ 7jun02 Local curvature Pores (sweat glands) 6 techieDetail16.ppt How is this data best used? Data class Macro-features Core & deltas Classical ridge minutia z Ridge endings z Bifurcations Generalized pattern z Cell matrices Specific pattern z Full pattern z Hot spots Fine structure z Ridge width patn z Pores DRS \\ 7jun02 Typical usage Limitations Sample alignment Large DB indexing (interfeature ridge counts) Efficient one-to-many matching Human-in-the-loop matching (e.g., FBI) Efficient one-to-one and one-to-few matching Best used with rolled finger images Deltas often out-of-frame in simple touch images Sometimes error prone in cracked elderly fingers Small sensor images have too few minutia. Emerging tech less understood Template size grows in one-tomany applications Small sensor matching One-to-one matching with Computation intensive low FAR May have larger template size Custom hardware assisted matching Small sensor matching Requires higher quality & more repeatable images Partial image latent prints Computation intensive 7 techieDetail16.ppt Commercial Fingerprint Matcher Trends Take advantage of the higher powered processors and higher quality images to match with small-area sensors z Utilize smaller & denser features – That are now sufficiently repeatable (in images from the best quality sensors) for use in matching – to achieve accurate matching with small-area sensors z Multiple view compositing (mosaic building) – Permits flexible finger positioning even on very small sensors Optimized for 1 to few matching z For personal computing and communication devices z Eliminates the complex structures used for large database searching and indexing DRS \\ 7jun02 8 techieDetail16.ppt For more information … Click here to learn how very small fingerprint sensors work Click here to learn about Specifying commercial biometric systems Back to Beginning DRS \\ 7jun02 9 techieDetail16.ppt
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