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BENCHMARKING FINGERPRINT ALGORITHMS

BENCHMARKING FINGERPRINT ALGORITHMS. Dr. Jim Wayman, Director US National Biometric Test Center San Jose State University email: biomet@email.sjsu.edu. CONSTRUCT MODEL CONDUCT EXPERIMENTS TO DERIVE MODEL PARAMETERS ERROR ANALYSIS SMALL SAMPLE SIZE GENERALIZABILTY OF SAMPLE POPULATION.

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BENCHMARKING FINGERPRINT ALGORITHMS

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  1. BENCHMARKING FINGERPRINT ALGORITHMS Dr. Jim Wayman, Director US National Biometric Test Center San Jose State University email: biomet@email.sjsu.edu

  2. CONSTRUCT MODEL CONDUCT EXPERIMENTS TO DERIVE MODEL PARAMETERS ERROR ANALYSIS SMALL SAMPLE SIZE GENERALIZABILTY OF SAMPLE POPULATION MAKING SCIENTIFIC PREDICTIONS

  3. COMPETING DESIGN REQUIREMENTS • THROUGHPUT RATE • NUMBER OF FALSE MATCHES • PROBABILITY OF FALSE NON-MATCH • HARDWARE COSTS

  4. 5 INTER-DEPENDENT OPERATIONAL PARAMETERS • HARDWARE COMPARISON RATE • PENETRATION RATE • BIN ERROR RATE • FALSE MATCH RATE • FALSE NON-MATCH RATE

  5. HARDWARE MATCH RATE • NUMBER OF COMPARISONS PER SECOND • 8,000 TO 300,000+ AVAILABLE • SEVERAL DOLLARS PER MATCH PER SECOND

  6. PENETRATION RATE • PERCENTAGE OF THE DATABASE THAT WILL BE COMPARED TO AVERAGE INPUT SAMPLE • “BINNING” BASED ON ENDOGENOUS MEASURES • “FILTERING” BASED ON EXOGENOUS MEASURES

  7. BIN ERROR RATE • MATCHING PRINTS PLACED IN DIFFERENT BINS • BIN ERRORS LEAD DIRECTLY TO FALSE NON-MATCHES • PENETRATION AND BIN ERROR RATE TRADE-OFF

  8. FALSE MATCH RATE • PROBABILTY THAT TWO COMPARED PRINTS WILL BE INCORRECTLY FOUND TO MATCH

  9. FALSE NON-MATCH RATE • PROBABILITY THAT TWO COMPARED PRINTS WILL BE INCORRECTLY FOUND NOT TO MATCH • COMPETES WITH FALSE MATCH RATE

  10. SYSTEM ERROR RATES • M INDEPENDENT PRINTS • FIRST-ORDER APPROXIMATIONS • ERROR BOUNDS

  11. SYSTEM THROUGHPUT • THROUGHPUT AND ERROR RATES LINKED TO PENETRATION • DOMINATED BY HUMAN FACTORS FOR SMALL-SCALE SYSTEMS

  12. SYSTEM EQUATIONS • FALSE NON-MATCH • FALSE MATCH • THROUGHPUT

  13. TESTING DATABASE • 4,080 “TRAINING” PRINTS • BEST QUALITY POSSIBLE • 80 IDENTIFIED “PRACTICE” PRINTS • 4,128 “TEST” PRINTS • BEST QUALITY EXPECTED IN OPERATION • 3,276 MATCH ONE OR MORE “TRAINING” PRINTS

  14. ANALYSIS • BINNING OF “TRAINING” PRINTS • BINNING OF “TEST” PRINTS • MATCHING RESULTS

  15. SMALL-SCALE VENDORS • SELF-SELECTED CATEGORY BASED ON ABILITY TO PERFORM 17 MILLION COMPARISONS • VENDORS SUPPLY COMPILED CODE • SCANNER SPECIFIC ALGORITHMS LIMIT USEFULNESS OF RESULTS

  16. SMALL-SCALE RESULTS

  17. CONCLUSIONS • SYSTEM MODELS ARE UNDERSTOOD • CONFIDENCE INTERVALS ARE BECOMING UNDERSTOOD • TESTING FROM CANNED DATABASES MAY NOT ALWAYS PRODUCE REASONABLE PERFORMANCE ESTIMATIONS

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