O’Connor, Kevin J. M.S., Purdue University, May, 2013. Examination of Stability in Fingerprint Recognition across Force Levels. Major Professor: Stephen J. Elliott.
In this thesis, the instability of zoo animal classifications for individuals across different force levels are illustrated, which answered the question, “Is an individual’s performance unstable with regards to the covariate under study in a fingerprint recognition system?” The covariate for this research was force levels (5 N, 7 N, 9 N, 11 N, and 13 N), in which 154 subjects interacted on a fingerprint device. The influence of applied force on the performance of a fingerprint algorithm was examined and supports in showing how zoo classifications change with the respected force levels. Zoo classifications have been used to group particular individuals as doves, worms, phantoms, chameleons, or normal. The purpose of the animal classifications was to determine whether subjects’ similarity score varies at different force levels and to quantify that instability by a score index. The stability score index formula (S.S.I) was used to calculate the stability for each individual from one force level to the next. This contribution can give researchers an idea of stability or instability for individuals performing on any biometric system.
Biometrics, Fingerprint recognition
Date of this Version
Technology, Leadership, and Innovation
Month of Graduation
Master of Science
Advisor 1 or Chair of Committee
Stephen J Elliott
Committee Member 1
Committee Member 2