The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% was obtained on the five-class problem. This performance evaluation was done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002).
Reference:
Msiza, IS, Leke-Betechuoh, B, Nelwamondo, FV and Msimang, N. 2009. Fingerprint pattern classification approach based on the coordinate geometry of singularities. 2009 IEEE International Conference on Systems, Man, and Cybernetics. San Antonio, Texas, USA, 11 - 14 October 2009, pp 516-523
Msiza, I., Leke-Betechuoh, B., Nelwamondo, F. V., & Msimang, N. (2009). Fingerprint pattern classification approach based on the coordinate geometry of singularities. Institute of Electrical and Electronics Engineering (IEEE). http://hdl.handle.net/10204/3769
Msiza, IS, B Leke-Betechuoh, Fulufhelo V Nelwamondo, and N Msimang. "Fingerprint pattern classification approach based on the coordinate geometry of singularities." (2009): http://hdl.handle.net/10204/3769
Msiza I, Leke-Betechuoh B, Nelwamondo FV, Msimang N, Fingerprint pattern classification approach based on the coordinate geometry of singularities; Institute of Electrical and Electronics Engineering (IEEE); 2009. http://hdl.handle.net/10204/3769 .
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