1. DRS \\ 7jun02 1 techieDetail16.ppt Fingerprint Matching Technology The Basics

2. DRS \\ 7jun02 2 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: AuthenTec supplied matchers AuthenTec supplied matchers Most independently available matchers Most independently available matchers Fingerprint matchers are catagorized: Primarily - by type of data used Primarily - by type of data used Secondarily – by method of comparing that data Secondarily – by method of comparing that data

3. DRS \\ 7jun02 3 techieDetail16.ppt Ridge Patterns Macro-features Macro-features –Core, deltas, scars Classical Ridge Minutia Classical Ridge Minutia Generalized Pattern Generalized Pattern Specific ridge pattern Specific ridge pattern Fine Structure Ridge shape Ridge shape –Lateral ridge shape –Vertical ridge shape Local curvature Local curvature Pores (sweat glands) Pores (sweat glands) What data is in a Fingerprint Image?

4. DRS \\ 7jun02 4 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns Macro-features Macro-features –Core, deltas, scars Classical Ridge Minutia Classical Ridge Minutia Generalized Pattern Generalized Pattern Specific ridge pattern Specific ridge pattern Fine Structure Ridge shape Ridge shape –Lateral ridge shape –Vertical ridge shape Local curvature Local curvature Pores (sweat glands) Pores (sweat glands)

5. DRS \\ 7jun02 5 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns Macro-features Macro-features –Core, deltas, scars Classical Ridge Minutia Classical Ridge Minutia Generalized Pattern Generalized Pattern Specific ridge pattern Specific ridge pattern Fine Structure Ridge shape Ridge shape –Lateral ridge shape –Vertical ridge shape Local curvature Local curvature Pores (sweat glands) Pores (sweat glands)

6. DRS \\ 7jun02 6 techieDetail16.ppt What data is in a Fingerprint Image? Ridge Patterns Macro-features Macro-features –Core, deltas, scars Classical Ridge Minutia Classical Ridge Minutia Generalized Pattern Generalized Pattern Specific ridge pattern Specific ridge pattern Fine Structure Ridge shape Ridge shape –Lateral ridge shape –Vertical ridge shape Local curvature Local curvature Pores (sweat glands) Pores (sweat glands)

7. DRS \\ 7jun02 7 techieDetail16.ppt How is this data best used? Data class Typical usage Limitations Macro-features Core & deltas Sample alignment Sample alignment Large DB indexing (inter- feature ridge counts) Large DB indexing (inter- feature ridge counts) Best used with rolled finger images Best used with rolled finger images Deltas often out-of-frame in simple touch images Deltas often out-of-frame in simple touch images Classical ridge minutia Ridge endings Ridge endings Bifurcations Bifurcations Efficient one-to-many matching Efficient one-to-many matching Human-in-the-loop matching (e.g., FBI) Human-in-the-loop matching (e.g., FBI) Sometimes error prone in cracked elderly fingers Sometimes error prone in cracked elderly fingers Small sensor images have too few minutia. Small sensor images have too few minutia. Generalized pattern Cell matrices Cell matrices Efficient one-to-one and one-to-few matching Efficient one-to-one and one-to-few matching Small sensor matching Small sensor matching Emerging tech less understood Emerging tech less understood Template size grows in one-to- many applications Template size grows in one-to- many applications Specific pattern Full pattern Full pattern Hot spots Hot spots One-to-one matching with low FAR One-to-one matching with low FAR Custom hardware assisted matching Custom hardware assisted matching Computation intensive Computation intensive May have larger template size May have larger template size Fine structure Ridge width patn Ridge width patn Pores Pores Small sensor matching Small sensor matching Partial image latent prints Partial image latent prints Requires higher quality & more repeatable images Requires higher quality & more repeatable images Computation intensive Computation intensive

8. DRS \\ 7jun02 8 techieDetail16.ppt Commercial Fingerprint Matcher Trends Take advantage of the higher powered processors and higher quality images to match with small-area sensors Utilize smaller & denser features 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 Multiple view compositing (mosaic building) Multiple view compositing (mosaic building) –Permits flexible finger positioning even on very small sensors Optimized for 1 to few matching For personal computing and communication devices For personal computing and communication devices Eliminates the complex structures used for large database searching and indexing Eliminates the complex structures used for large database searching and indexing

9. DRS \\ 7jun02 9 techieDetail16.ppt For more information … Back to Beginning Click here to learn how very small fingerprint sensors work Click here to learn about Specifying commercial biometric systems