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(Fall 2012) Recognition of Henry Classifications Between Fingerprint Sensors
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(Fall 2012) Recognition of Henry Classifications Between Fingerprint Sensors

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The question that is trying to be answered is if Henry Classifications of fingerprints will be recognized differently across multiple sensors. Answering this would help to understand the reliability …

The question that is trying to be answered is if Henry Classifications of fingerprints will be recognized differently across multiple sensors. Answering this would help to understand the reliability of different sensors and the overall compatibility of different sensors. When purchasing new sensors, it would be beneficial to know which sensor has the least amount of variability between classifications. This would also be useful when upgrading hardware to ensure that it is compatible with an existing database. To do this, we compared the Henry Classifications of different sensor results and analyzed the variability found. The variability that we found was due to either user error, or an error with the algorithm used with the sensor.

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  • 1. RECOGNITION OF HENRY CLASSIFICATIONS BETWEEN FINGERPRINT SENSORS The question that is trying to be answered is if Henry Classifications of fingerprints will be recognized differently across multiple sensors. Answering this would help to understand the reliability of different sensors and the overall compatibility of different sensors. When purchasing new sensors, it would be beneficial to know which sensor has the least amount of variability between classifications. This would also be useful when upgrading hardware to ensure that it is compatible with an existing database. To do this, we compared the Henry Classifications of different sensor results and analyzed the variability found. The variability that we found was due to either user error, or an error with the algorithm used with the sensor. Keenan Winters, Zach Moore, Andrew Feder, Michael Brockly, Stephen Elliott Overview 0 10 20 30 40 50 60 70 80 90 VariationofClassification (#ofSubjects) Sensor • Started with 10,129 sensor reads • Cleaned 1,327 reads (28 subjects) • Left with 8,802 reads (173 subjects) • Each sensor type has 978 reads • Each subject has 54 reads Data Cleaning • Variation of Classification • Six scans for each sensor • Nine sensors for each subject • Yes = Variation • No = No Variation • Yes = 405 • No = 1062 • Figure one is a correct scan and is classified as a whorl • Figure two is recognized incorrectly as right slant • Variation is caused from incomplete image 1. 2. Examples of Variation 3. 4. • Both figure three and figure four are scanned similarly • Figure three was classified as scar • Figure four and rest of samples were classified as right slant loop Variation Henry Classification System • The Henry Classification System organizes fingerprint records by pattern type • These patterns are divided into five basic groups: • Arch: a ridge that runs across the fingertip and curves up in the middle • Whorl: an oval formation, often making a spiral pattern around a central point • Loops: these have a stronger curve than arches, and they exit and enter the print on the same side • Composites: are a mix of other patterns • Accidentals: form an irregular pattern that’s not classifiable as an arch, loop or whorl • Tented Arch • Plain Whorl • Central Pocket Whorl • Right Loop • Left Loop Observations • As seen in the chart, the Authentec sensor showed the most variability • Crossmatch showed the least variability • Variability was caused by either user error or algorithm error • User error was caused by incorrect placement of the finger on the sensor • Algorithm error occurred when the image had a certain classification, but the algorithm used to extract it classified it incorrectly Before analyzing the data, it first had to be cleaned. Cleaning the data was necessary because some of the subjects weren’t enrolled on all of the sensors. We only used those subjects who used all of the sensors. Some also had more or less reads than others. Each subject was enrolled on nine different sensors. Their right index finger was read six separate times for each sensor. If any of the six reads had different Henry Classifications, that set was flagged as having variation. Recommendations for Performance • Using a Crossmatch sensor for classifying fingerprints would have the least variability • When enrolling subjects, it is important to have a standard method of enrollment to reduce user error Subject Variation Per Sensor Subgroups Roberts, C. (2006). Biometric Technologies-Fingerprints., (pp. 4-5, 12-15).