EVALUATION OF MINUTIAE POINTS ACROSS SENSORS
The project looked to determine if minutiae point count differed when measure...
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(Fall 2012) Evaluation of Minutiae Points Across Sensors

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The project looked to determine if minutiae point count differed when measured across different sensors and algorithms. Data from nine different sensors of various types and data collection methods were collected, and minutiae points were calculated using both Megamatcher and Aware algorithms. After the data was collected, it was examined to determine standard deviation, minutiae point count, and any similarities between sensors that show a positive correlation. This research may be used in the future to determine if quality is affected by minutiae count, which may become a method for sensor and algorithm selection. It may also be used to analyze the interoperability of different sensors to see if significant data loss is to be expected during data transfer.

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(Fall 2012) Evaluation of Minutiae Points Across Sensors

  1. 1. EVALUATION OF MINUTIAE POINTS ACROSS SENSORS The project looked to determine if minutiae point count differed when measured across different sensors and algorithms. Data from nine different sensors of various types and data collection methods were collected, and minutiae points were calculated using both Megamatcher and Aware algorithms. After the data was collected, it was examined to determine standard deviation, minutiae point count, and any similarities between sensors that show a positive correlation. This research may be used in the future to determine if quality is affected by minutiae count, which may become a method for sensor and algorithm selection. It may also be used to analyze the interoperability of different sensors to see if significant data loss is to be expected during data transfer. Ryan Lovan, Steven Titus, Zach Eastman, Michael Brockly, Stephen Elliott Overview OVERALL RESULTS The Aware algorithm consistently had higher mean minutiae point count than Megamatcher’s minutiae point count, as well as a higher standard deviation. Aware Tukey Method • Authentec and Fujitsu had similar mean minutiae count. • Crossmatch and UareU are both optical sensors, and had similar mean minutiae count. • UPEKslap and Futronic share similar mean minutiae counts, but seem to share no commonalities. • All other sensors not mentioned had significantly different minutiae counts. • P-Values were calculated to be 0.000 Megamatcher Tukey Method • UPEkswipe, Authentec, and Fujitsu are all Capacitive sensors, and had similar mean minutiae count. • Atmel and Futronic also had similar mean minutiae count, but there do not seem to be any similarities between sensors. • All other sensors not mentioned had significantly different minutiae counts. • P-values were calculated to be 0.000 Matcher Type Color Aware Megamatcher LIST OF SENSORS We used these sensors for our data collection. The sensors cover many different sensing types and actions. Grouping Megamatcher Information Using Tukey Method Sensor Samples Mean Minutiae Grouping Atmel 1135 42.981 A Futronic 1123 42.645 A Crossmatch 1315 40.52 B UPEkslap 1123 36.856 C UareU 1111 32.105 D Identix 1113 29.149 E UPEkswipe 1110 27.091 F Authentec 1007 26.607 F G Fujitsu 1099 25.632 G Means that do not share a letter are significantly different. Sensor # of Sample Images Mean Minutiae Point Count Standard Deviation Atmel 1134 73.76 42.98 18.90 12.61 Authentec 1006 31.06 26.61 11.92 10.74 Crossmatch 1314 41.44 40.52 15.10 10.67 Fujitsu 1098 29.94 25.63 9.167 8.193 Futronic 1123 47.68 42.64 11.77 11.23 Identix 1112 33.88 29.15 8.789 8.228 UareU 1110 39.99 32.11 9.860 8.553 UPEKslap 1122 48.46 36.86 12.82 9.713 UPEKswipe 1110 37.65 27.09 11.02 6.684 Sum 10129 ---------- ---------- ---------- ---------- DATA ANALYSIS OBSERVATIONS Dataset Name Fingerprint Sensor Type of Sensor Action Capture Area (mm) Atmel Atmel Fingerchip Thermal Swipe 14 x .4 Authentec Authentec AES2501 Capacitive Swipe 13.8 x 5 Crossmatch CrossMatch Verifier LC 300 Optical Touch 30.5 x 30.5 DP- UareU DigitalPerona U.are.U 4000a Optical Touch 14.6 x 18.1 Fujitsu Fujitsu MBF 230 Capacitive Touch 12.8 x 15 Futronic Futronic FS80 Optical Touch 16 x 24 Identix Intentix DFR 2080 Optical Touch 15 x 15 UPEK_S UPEK Eikon Capacitive Swipe 12.4 x .2 UPEK_T UPEK TCS1 Capacitive Touch 12.8 x 18 We analyzed the data from the Shimon Interoperability database and constructed histograms from the minutiae counts of each sensor. The boxplots below compare the overall results of each histogram. Grouping Aware Information Using Tukey method Sensor Samples Mean Minutiae Grouping Atmel 1134 73.76 A UPEKslap 1122 48.46 B Futronic 1123 47.68 B Crossmatch 1314 41.44 C UareU 1110 39.99 C UPEKswipe 1110 37.65 D Identix 1112 33.88 E Authentec 1006 31.06 F Fujitsu 1098 29.94 F Means that do not share a letter are significantly different Conclusion: Based on our observations and the data analysis (as shown on the left), there are significant differences both between different sensors using the same algorithm and different algorithms using the same sensors. These differences need to be addressed to further interoperability in fingerprinting technologies.

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